14th European Congress on Digital Pathology
and the 5th Nordic Symposium on Digital Pathology

 

29th May - 1st June, 2018, Helsinki, Finland

About the Conference

The primary goal is to bring together researchers, clinicians and industry working in the field of digital pathology, to present and discuss science, implementation of digital techniques and the latest advances in the field.

The congress theme is digital diagnostics and intelligence augmentation, with focus on artificial intelligence for pathology. The meeting also includes sessions on standards, quality management, clinical workflow integration, integrative pathology, translational research and precision medicine.

Conference History

This is the 14th European Congress on Digital Pathology. The first was organized in Heidelberg (Germany, 1992) and the three most recent ones in Berlin, Paris, Venice. The conference is also the 5th Nordic Symposium on Digital Pathology, the four previous being arranged in Linköping, Sweden.

About organizers

The main organizer is the Institute for molecular medicine Finland – FIMM, University of Helsinki and endorsed by the European Society of Digital and Integrative Pathology.

Conference Speakers

The speakers are leading experts and researchers in the field of digital pathology, translational research, point-of-care pathology and artificial intelligence applied to diagnostics.

Speakers

Posters

Event Info

Organizers

Sponsors

Exhibitors

Schedule

  • 29 May
  • 30 May
  • 31 May
  • 01 June

Klaus Kayser

Professor, Dr.
Charité, University of Berlin
Klaus Kayser, Professor at Charité, University of Berlin is one of the European pioneers of digital pathology and telepathology, and organizer of the first European Congress of Telepathology (current ECDP) hosted in Heidelberg, Germany in 1992. Prof. Kayser will give a presentation on “Cognitive algorithms for tissue-based diagnosis” during the opening ceremony of the meeting.

29 May

30 May

Greg Corrado

Director of Augmented Intelligence Research, Co-founder of Google Brain
Google Inc
Title: The Opportunity for Machine Intelligence in Digital Medicine

Biography:
Greg Corrado, Director of Augmented Intelligence Research at Google
is a senior researcher in artificial intelligence, computational neuroscience, and machine learning. He has published in fields ranging across behavioral economics, particle physics, systems neuroscience, and deep learning. At Google he served as one of the founding members of and co-lead of the Google Brain Project for large scale deep neural networks. Dr. Corrado will give a presentation on the future of machine learning.

30 May

David Clunie

MBBS, FRANZCR (Ret), FSIIM
PixelMed Publishing
Title

Abstract
As WSI begins to permeate routine clinical anatomical pathology workflow globally, confusion reigns as to the role of standard rotocols and formats for image interchange and storage, as opposed to proprietary monolithic systems. Though the regulatory issues of modular systems remain to be resolved, standards do exist and are being implemented, as the success of recent DICOM Connectathons demonstrates. The lessons learned from other specialties about the advantages and disadvantages of DICOM are considered for a range of use cases. Decisions in the design of the DICOM WSI standard are considered in light of experience with similar proprietary formats. The role of meta data and the importance of anticipating annotation interchange is also examined, particularly from the perspective of automated and interactive analytic applications. High volume operational use requires attention to the workflow and appropriate acquisition and reporting management standards need to be selected and implemented. Enterprise integration and cross enterprise data exchange are key considerations that are also predicated on the successful deployment of standards.

Biography
David Clunie is a retired neuroradiologist, medical informaticist, DICOM open source software author, editor of the DICOM standard and independent consultant. He was formerly the co-chair of the IHE Radiology Technical Committee and industry co-chairman of the DICOM Standards Committee, as well as being a member or chair of many of the DICOM working groups, including structured reporting, digital x-ray, compression, interchange media, base standard, display, mammography, security, application hosting, clinical trials, small animal imaging and digital pathology. Recently he has specialized in the technical aspects of enterprise imaging for non-radiological specialties and has provided technical support for the whole slide imaging Connectathons organized by DICOM WG 26, specializing in the validation of compliance of WSI implementations with the DICOM standard.

31 May

31 May

31 May

Carolina Wählby

Professor
Uppsala University, Dept of Information Technology
Title
Image based in situ sequencing as a basis for learning tissue morphology

Biography
Carolina Wählby, Professor at the Department of Information Technology, Uppsala University is head of the Centre for Image Analysis at Uppsala University and focusing on digital image processing and analysis in the interface between biomedicine, microscopy, and computer science. She leads a number of research projects involving large-scale cell and tissue analysis, including computational methods for spatial transcriptomics, funded by the ERC and the Swedish Foundation for Strategic research. Prof. Wählby will give an overview of recent projects related to tissue analytics and machine learning for pathology.

31 May

31 May

Michael Feldman

Professor, Vice Chairman Clinical Services, Pathology and Laboratory Medicine
University of Pennsylvania School of Medicine
Title
A Byte of the future: Digital Pathology and Machine Learning, New Value Based Opportunities

Abstract
Digital Pathology is a broad term that is often associated with just whole slide imaging but truly encompasses far more. In Europe, Digital Pathology has enjoyed CE mark for routine diagnostics and has seen full scale adoption in a growing number of practices across multiple countries. In the USA, the FDA recently cleared the first whole slide imaging system for primary diagnostic with more companies to follow and the first clinical site is moving toward adoption of whole slide imaging for diagnsotics. New image acquisition modalities are developing which further push “Digital Pathology” in new directions (Computational photonics) as well as acquisition methodologies for “slideless” imaging of tissue present new and exciting opportunities for us to rethink what we mean by the term “digital pathology”. Of course, no presentation of “digital pathology” would be complete without a discussion of the implication of machine learning within this space. A value focused discussion with specific use cases of machine learning and deep learning to create a path towards adoption will be presented as these technologies begin to mature from basic science laboratories and move into clinical practice. Finally, a brief discussion of integrated multiscale imaging across radiology and pathology will be discussed, again with an eye on value to the health system as well as value to our patients

Biography
Michael Feldman is Professor of Pathology and Laboratory Medicine at the Hospital of the University of Pennsylvania. His professional interests revolve around the development, integration and adoption of information technologies in the discipline of Pathology. One of his main areas of interest within this broad discipline has been in the field of digital imaging. He has been studying pathology imaging on several fronts including interactions between pathology/radiology (Radiopathogenomics of prostate cancer and breast carcinoma), development and utilization of computer assisted diagnostic algorithms for machine vision in prostate and breast cancer. More recently his team has been developing deep learning methods for complex interrogation of pathology slides both within the cancer domain as well as in cardiovascular and renal pathology. Prof. Feldman and his collaborators have also been developing methods to apply multispectral imaging for the analysis of multiplexed immunohistochemistry and immunoflourescence to tissues along with the development of a quantitative system for scoring and analyzing these studies at a cytometric level on surgical pathology slides. The efforts have been recognized by the national funding agencies of the NIHand DOD as well as industry sponsored projects.
Prof. Feldman will give a presentation on recent advances in digital pathology, in particular related to validation studies for FDA approval.

30 May

31 May

31 May

Fredrik Pontén

Professor
Uppsala University
Fredrik Pontén, Professor at Uppsala University is a board-certified senior physician and specialist of Anatomical Pathology. Prof. Pontén is co-founder and the Vice-Program and Clinical Director of the Human Protein Atlas. His research is focused on gene expression profiling in normal and cancer tissues with emphasis on translational medicine. Prof. Pontén will give a talk entitled “The Human Protein Atlas - a digital tool for pathology and clinical medicine”.

01 June

Liron Pantanowitz

Professor of Pathology and Biomedical Informatics
UPMC University of Pittsburgh
Title
Does Pathologist Input for AI Apps Matter?

Abstract
This an exciting but unnerving time in pathology in which we are witnessing the emergence of AI algorithms that can accurately analyze and interpret pathology images. Several academic computational biologists and vendors, including many AI start-up companies are now focused on AI. Pathologists are accordingly being increasingly asked to participate in co-developing these algorithms for digital pathology. The intent of this talk is to address some of the key questions about developing computational pathology algorithms from a pathologist’s perspective. These questions include: (1) do we use a traditional machine learning or deep learning approach?, (2) how much data is needed to build a good algorithm?, (3) why and who needs to annotate images?, (4) how does one get other pathologists to use this algorithm?, and (5) does this app need regulatory clearance if it is intended for clinical use?

Biography
Dr. Liron Pantanowitz is a Professor of Pathology and Biomedical Informatics at the University of Pittsburgh in the USA. He is the Director of Pathology Informatics and Cytopathology at the University of Pittsburgh Medical Center. Dr. Pantanowitz is an Editor-in-Chief of the Journal of Pathology Informatics. He is a member of the Association for Pathology Informatics council, College of American Pathologist's digital pathology committee, and Digital Pathology Association board of directors. He is well published and has written several textbooks in informatics, including digital pathology.

30 May

Liron_Pantanowitz_Abstract.pdf Download Link

Johan Hartman

Associate Professor
Karolinska Institutet
Title: Digital image analysis in breast pathology to facilitate personalised medicine

Abstract:
Malignant cell proliferation remains as a paramount prognostic indicator in breast cancer. Moreover, cancer cell proliferation is associated with therapeutic response. Tumor cell proliferation can be detected by direct assessment of mitotic figures or associated biomarkers. The most frequent biomarker in routine diagnosis and research is Ki67 but its assessment is hampered by poor reproducibility. We have earlier shown that Ki67 analysis is dramatically improved by digital image analysis. Moreover, digital image analysis can stratify patients into subgroups with both prognostic and predictive relevance. Recent developments in machine learning enable identification of relevant morphological patterns and even more sophisticated methods to stratify patients. In combination with cancer sequencing, these methods will be fundamental to facilitate precision medicine in oncology.

Biography:
Johan Hartman, MD, PhD is breast pathologist at Karolinska University Laboratory, Stockholm, Sweden. He is a board member of the Stockholm Medical Biobank and leads the task force in precision medicine at the personalised medicine program at Karolinska Institutet. He leads the national quality and standardisation committee in breast pathology (KVAST-bröst), consisting of expert breast pathologists with responsibility for composing national guidelines.Dr Hartmans research team performs translational breast cancer research with focus on precision medicine. This includes digital image analysis, clinical cancer sequencing and patient-derived ex vivo models to predict drug response in patients.

30 May

30 May

Harry B. Burke

Professor of Medicine
F. Edward Hébert School of Medicine, Uniformed Services University
Title
Deep Digital Convergence: Pathology, Radiology, and Molecular Medicine in the 21stCentury


Abstract
For most of the 20thCentury pathology, radiology, and molecular medicine were independent clinical domains. Each existed in its own ocular realm – radiologists looked at structural and functional anatomic images, pathologists looked at tissue images, and molecular medicine looked at biochemical false-color microarrays.

In the 21stCentury, the information in these domains has become, or is rapidly becoming, digital. The transition from the ocular to the digital has created electronic data types that, for many medical activities, allows the convergence of these three information levels of analysis to create a new integrated information paradigm, one that comprises digital radiology, digital pathology, and digital molecular medicine (D3RPM). D3RPM is an integrated multi-dimensional model that significantly extends the power of clinical medicine. Each dimension contributes orthogonal information and together they are greater than the sum of their dimensions. The result is an multilevel structural, multicellular, and molecular model of disease. Together, they are the core of precision medicine.

But I have gotten ahead of myself; we have not yet integrated these three information levels of analysis to create a coherent information system, and we have not yet integrated this system into clinical medicine. In order to clinically operationalize D3RPM we will have to build electronic clinical decision support systems (CDSS) that: (1) function in the medical domains of risk/prevention, diagnosis, and prognosis/treatment, (2) contain powerful predictive analytics for accurate individualized patient predictions and include deep learning algorithms within and across patients that improve its performance, (3) construct models using D3RPM information and other relevant clinical data, (4) acquire and integrate into these models new clinical information acquired in real time during the clinician-patient encounter, and (5) effectively communicate to the clinician and patient in real time the model-generated patient-centric clinical information necessary for improved shared decision-making. This approach has the potential to move precision medicine from an interesting idea at the bench to a powerful clinical instrument at the bedside.

Biography
Harry B. Burke, MD, PhD, was awarded medical and doctoral degrees by the University of Chicago. He is a Professor of Medicine at the Uniformed Services University of Health Sciences, Bethesda, MD, USA. His research interests include advanced statistical modeling and predictive analytics, applied information theory, health informatics including clinical decision support systems and natural language processing, and the use of molecular biomarkers for clinical outcome prediction. He has an extensive publication record, he is a member of the Editorial Board of several journals, including the Journal of the National Cancer Instituteand the American Cancer Society’s journal Cancer, and he reviews for many high impact journals.

31 May

Lee Cooper

Assitant Professor of Biomedical Informatics
Emory University School of Medicine
Title:
Machine learning and software infrastructure for prognostication and discovery in pathology

Abstract:
Predicting the future course of a patient’s disease is critical in choosing therapy and in helping patients to plan their lives. Despite the rich data produced by genomic and imaging platforms, the accuracy of prognoses for patients diagnosed with cancer can be highly variable, often relying on a handful of molecular biomarkers or subjective interpretations of histology. In this talk, Dr. Cooper will discuss recent advances in combining conventional survival models with deep learning techniques to build machines that can predict patient survival from histology and genomics. He will also discuss how open-source platforms for digital pathology can play an important role in facilitating the development of the next generation of digital pathology algorithms, and can overcome challenges in satisfying the need for training data.

Biography:
Lee Cooper, PhD, is an Assistant Professor of Biomedical Informatics and Engineering at the Emory University School of Medicine and Georgia Institute of Technology in Atlanta, Georgia, USA. His research focuses on machine-learning methods for predicting clinical outcomes from integrated histology and genomic data, and the development open-source software infrastructure that allows clinicians and investigators to interact with complex pathology datasets and learning algorithms.

30 May

30 May

Nasir Rajpoot

Professor
University of Warwick
Title
The Promise of Computational Pathology

Abstract
The human brain is fantastic at recognising people and objects and building an understanding of the natural world around us. However, the visual cortex is not ideal at objectively measuring what we see and complex spatial patterns hidden in plain sight cannot sometimes be deciphered by the naked eye. Computational Pathology is an emerging discipline concerned with the study of computer algorithms for understanding disease from the analysis of digitised histology images. I will show some snippets of computational pathology research in my group to demonstrate the value of analytics of information-rich whole-slide images (the so-called Big Cancer Image Data) for cancer diagnosis and prognosis. I will show examples of how histological motifs extracted from digital pathology image data are likely to lead to patient stratification for precision medicine. I will conclude with some of the main challenges facing digital pathology research.

Biography
Nasir Rajpoot is Professor in Computational Pathology at the University of Warwick, where started his academic career as a Lecturer (Assistant Professor) in 2001. At Warwick, he is the founding Head of Tissue Image Anayltics (TIA) lab since 2012. He also holds an Honorary Scientist position at the Department of Pathology, University Hospitals Coventry & Warwickshire NHS Trust since 2016. The focus of current research in his lab is on developing novel computational pathology algorithms with applications to computer-assisted grading of cancer and image-based markers for prediction of cancer progression and survival.

Prof Rajpoot has been active in the digital pathology community for almost a decade now, having co-chaired several meetings in the histology image analysis (HIMA) series since 2008 and served as a founding PC member of the SPIE Digital Pathology meeting since 2012. He was the General Chair of the UK Medical Image Understanding and Analysis (MIUA) conference in 2010, and the Technical Chair of the British Machine Vision Conference (BMVC) in 2007. He has guest edited a special issue of Machine Vision and Applications on Microscopy Image Analysis and its Applications in Biology in 2012, and a special section on Multivariate Microscopy Image Analysis in the IEEE Transactions on Medical Imaging in 2010. He is a Senior Member of IEEE and member of the ACM, the British Association of Cancer Research (BACR), and the European Association of Cancer Research (EACR).

Prof Rajpoot was recently awarded the Wolfson Fellowship by the UK Royal Society and the Turing Fellowship by the Alan Turing Institute, the UK’s national data science institute. He will be chairing the European Congress on Digital Pathology (ECDP) at Warwick in 2019.

30 May

31 May

Gloria Bueno

Principal Researcher
University of Castilla-La Mancha
Title
Experiences of Digital Diagnostics in Practice

Abstract
The future paradigm of pathology will be digital. Instead of conventional microscopy, a pathologist will perform a diagnosis through interacting with images on computer screens and performing quantitative analysis. The fourth generation of virtual slide telepathology systems, so-called virtual microscopy and whole-slide imaging (WSI), has allowed for the storage and fast dissemination of image data in pathology and other biomedical areas. These novel digital imaging modalities encompass high-resolution scanning of tissue slides and derived technologies, including automatic digitization and computational processing of whole microscopic slides. Moreover, automated image analysis with WSI can extract specific diagnostic features of diseases and quantify individual components of these features to support diagnoses and provide informative clinical measures of disease. Therefore, the challenge is: 1at) to apply information technology and image analysis methods to exploit the new and emerging digital pathology technologies effectively in order to process and model all the data and information contained in WSI and 2nd) To adopt the developed tool into clinical practice, which is still the major challenge. The final objective is to support the complex workflow from specimen receipt to anatomic pathology report transmission, that is, to improve diagnosis both in terms of pathologists' efficiency and with new information. This talk will present two tools developed by VISILAB group and currently used in clinical practice and research at INCLIVA and HGUCR. The tools are based on both deep learning approached and classical methods and they are dedicated to Her2 quantification and angiogenesis research and neuroblastoma analysis, respectively.

Biography
Dr. Gloria Bueno Garciais Professor at the Engineering School of University of Castilla-La Mancha where lead VISILAB research group dedicated to machine vision and artificial intelligence applications. She holds a PhD in Machine Vision from Coventry University, UK (1998). She has experience working as principal researcher in several research centres such as UMR 7005 research unit CNRS/ Louis Pasteur Univ. Strasbourg (France), Gilbert Gilkes & Gordon Technology (UK) and CEIT San Sebastian (Spain). She is the author of 2 patents, 4 registered software and more than 80 refereed papers in journals and conferences. Runner-up award for the best PhD work on computer vision & pattern recognition by AERFAI and the 'Image File & Reformatting Sofware' Challenge Award. She has served as visiting researcher at Carnegie Mellon University (USA), Imperial College London (UK) and Leica Biosystems (Ireland). She is a Senior Member of IEEE and is affiliated with several societies such as ESDIP relevant to the topic of ECDP. In the field of Digital Pathology she is the coordinator of the European AIDPATH project entitled ‘Academia and Industry Collaboration for Digital Pathology’ composed of 11 partners from both private and public sector.

01 June

01 June

Peter Mildenberger

Professor
University Medical Center Mainz
Title
Digital Imaging Adoption Model (DIAM): a joined Initiative of HIMSS and ESR (European Society of Radiology) to promote advanced IT solutions in Medical Imaging

Abstract
IT support in Radiology is well established. Many different tools and systems are available. Therefore, some guidance for users should be helpful for planning and decision making in the implementation of imaging informatics. Jointly developed by HIMSS Analytics® and the European Society of Radiology (ESR), the Digital Imaging Adoption Model (DIAM) helps to evaluate the maturity of IT-supported processes in medical imaging in both hospitals and diagnostic centres. This eight-stage maturity model drives your organisational, strategic and tactical alignment towards imaging-IT maturity. With its standardised approach, it enables regional, national and international benchmarking. Participants will receive a gap analysis with detailed action items for future investment decisions. In addition, DIAM provides an authoritative basis for presentation to the management level.

Biography
Peter Mildenberger, MD, is an associated professor for Radiology at the University Medical Center in Mainz (Germany). Besides the clinical focus on diagnostic imaging in cardio-vascular, emergency and uro-radiology, he is very much engaged in imaging informatics. Actual activities are focused on Structured Reporting and machine learning in Radiology. He is also very active in several organisations, e.g. as chair of the ESR subcommittee on "Professional Issues and Economics in Radiology" and chair of the joint RSNA-ESR "Template Library Advisory Panel". He is also representing ESR in the DICOM Standards Committee and in IHE.

31 May

Isaac Bogoch

Associate Professor of Medicine
University of Toronto
Title: Mobile Phone and Handheld Microscopy for Global Health Applications

Abstract:
There is limited laboratory infrastructure and laboratory capacity in low-income countries, especially in rural areas. Infectious and non-infectious diseases are more common in these settings and preferentially affect the poorest of the poor. Novel solutions to improve the quality of clinical and Public Health care delivery are required. Mobile phone and handheld microscopy may enable diagnostic support in such settings. These devices have several attractive attributes in that they are low cost, battery powered, and portable. More sophisticated equipment can harness technological features such as image recognition for automated diagnoses, and GPS monitoring to map diseases in a region. This talk focuses on the development and implementation of mobile phone and handheld microscopes for use in clinical and Public Health settings in Africa that are endemic for infectious diseases, such as malaria, schistosomiasis, and other worm infections. The strengths and limitations of diagnostic devices, in addition to future directions will be discussed in detail.

Short biography:

Dr. Isaac Bogoch is an Assistant Professor at the University of Toronto in the Department of Medicine, and is an Infectious Diseases consultant and General Internist at the Toronto General Hospital. Dr. Bogoch divides his clinical and research time between Canada and several countries in Africa and Asia. He collaborates with a team that models the spread of emerging infectious diseases. In addition, Dr. Bogoch studies innovative and simple diagnostic solutions to improve the quality of care in resource-constrained settings, including implementing mobile phone and handheld microscopy for clinical and public health applications.

30 May

Francesco Ciompi

Postdoc Researcher
Radboud University Nijmegen
Title
Computational Pathology and Artificial Intelligence
Abstract
Computational Pathology embodies the synergy of Digital Pathology, Medical Image Analysis, Computer Vision, and Machine Learning. The huge amount of information and data available in multi-gigapixel histopathology images makes digital pathology the perfect use case for advanced image analysis techniques. For this reason, deep learning and artificial intelligence have successfully powered computational pathology research in recent years.
In this talk, I will present some of our recent research results in deep learning and computational pathology, and discuss open challenges.
Short bio
Dr. Francesco Ciompi is a senior researcher in Computational Pathology at Radboud University Medical Center, Nijmegen (Netherlands).
He received the Master's degree in Electronic Engineering from the University of Pisa in July 2006 and the Master's degree in Computer Vision and Artificial Intelligence from the Autonomous University of Barcelona in September 2008.
In July 2012 he obtained the PhD (cum laude) in Applied Mathematics and Analysis at the University of Barcelona, with a thesis on "Multi-Class Learning for Vessel Characterization in Intravascular Ultrasound". In February 2013 he joined the Autonomous University of Barcelona as postdoctoral researcher, working on machine learning for computer vision and large scale image classification and retrieval. From September 2007 to September 2013 he was also member of the Computer Vision Center.
From October 2013 to September 2015, he was a postdoctoral researcher at the Diagnostic Image Analysis Group of Radboud University Medical Center, Nijmegen.
His research focuses on deep learning for the analysis of medical images in cancer research, with a particular focus on development of prognostic and predictive imaging biomarkers based on histopathology image analysis.

30 May

01 June

Peter Hufnagl

Head Department of Digital Pathology and IT
Charité University Hospital Berlin
Title
Deep Learning and Augmented Reality may bridge the gap between conventional and digital pathology

Abstract
The use of conventional light microscopes and digital pathology seem to be mutually exclusive. On the one hand, complete digitization offers many advantages. On the other hand, the digital workflow requires the timely and complete scanning of all slides. This requires not only a change in the pathologist's workflow, but also a corresponding investment that many institutions shy away from. Can one still benefit from digitization while retaining the conventional way of working? Yes, there are several possibilities here. Some institutions practice a successful coexistence of conventional and digital working methods. As a rule, cases are assigned according to indication or personnel. Of particular interest is the addition of conventional microscopy through the online creation of WSI, Deep Learning and Augmented Reality. For this purpose, the light microscope must first be supplemented by a fast camera. The resulting images can either be used to create a WSI in the background, for example to obtain a second opinion. Alternatively, the images can be analyzed immediately on the computer and the results reflected live into the light microscope. Thus mitoses can be counted or tumor areas can be visualized. Such a retrofitting of a microscope is comparatively inexpensive compared to a complete conversion to WSI. The advantage: A smooth transition from conventional to digital becomes possible.

Biography
Peter Hufnagl, PhD, is head of Digital Pathology at the Charité – Universitätsmedizin Berlin, Germany. His research interest is in combining different technologies with the aim of helping patients individually. This includes conventional image analysis and telemedical systems as well as biobanking and the use of artificial intelligence. Since 2017, Dr. Hufnagl has also been head of the Center for Biomedical Image and Information Processing (CBMI) with a focus on the application of artificial intelligence. Dr. Hufnagl will explain how digital methods can also be used successfully with conventional light microscopes.

30 May

30 May

Arvydas Laurinavičius

Professor
Vilnius University
Title
Getting pathology pixels to work

Abstract

Digital image processing technologies and analytics promise outburst of new knowledge and practical implementations in tissue pathology. Microscopy slides contain abundant biological data which can be retrieved in multiple tissue staining and imaging modalities. Recent advance of artificial intelligence brings a new wave to the innovations. While the digital technologies are maturing and new computational pathology analytics are being developed, major effort is needed to promote clinical validation and implementation of the tools. As an example, our pilot implementation of comprehensive Ki67 immunohistochemistry analytics for routine breast cancer diagnosis revealed an added value in quality assurance of pathologist’s evaluation of the proliferation index. The tool also provided an advice on the “hottest” area of the tumor tissue based on hexagonal grid analytics; however, this aspect of the experiment led to the discussions between the participating pathologists on the concept of the hot spot. Ironically, this concept is widely used but has multiple, often obscure definitions in research papers and clinical guidelines. We suggest that “subvisual” features such as hot spots, even if defined for “human use”, can hardly be validated and reproducibly evaluated by human observers. Instead, automated and robust computational, applications validated against each other and clinical outcomes, could promote clinical adoption of the decision support tools.

Biography
Arvydas Laurinavicius, MD, PhD is Pathology Professor at Vilnius University and Director of the National Center of Pathology, affiliate of the Vilnius University Hospital Santara Clinics, Lithuania. His research focuses on digital image analytics to derive novel tissue pathology indicators for disease modelling. In particular, Prof. Laurinavicius has together with his research group at Vilnius University and researchers at Caen and Nottingham Universities developed methodologies for comprehensive digital immunohistochemistry analytics to empower information retrieval from routine IHC slides. Prof. Laurinavicius will share recent experiences in integrating potential decision support tools into diagnostic pathology workflow and perspectives on further developments needed to achieve robust clinical applications.

30 May

01 June

Anant Madabhushi

Professor of Biomedical Engineering
Case Western Reserve University
Title: Computational Pathology as a companion diagnostic: Implications for Precision Medicine

Abstract
With the advent of digital pathology, there is an opportunity to develop computerized image analysis methods to not just detect and diagnose disease from histopathology tissue sections, but to also attempt to predict risk of recurrence, predict disease aggressiveness and long term survival. At the Center for Computational Imaging and Personalized Diagnostics, our team has been developing a suite of image processing and computer vision tools, specifically designed to predict disease progression and response to therapy via the extraction and analysis of image-based “histological biomarkers” derived from digitized tissue biopsy specimens. These tools would serve as an attractive alternative to molecular based assays, which attempt to perform the same predictions. The fundamental hypotheses underlying our work are that: 1) the genomic expressions detected by molecular assays manifest as unique phenotypic alterations (i.e. histological biomarkers) visible in the tissue; 2) these histological biomarkers contain information necessary to predict disease progression and response to therapy; and 3) sophisticated computer vision algorithms are integral to the successful identification and extraction of these biomarkers. We have developed and applied these prognostic tools in the context of several different disease domains including ER+ breast cancer, prostate cancer, Her2+ breast cancer, ovarian cancer, and more recently medulloblastomas. For the purposes of this talk I will focus on our work in breast, prostate, rectal, oropharyngeal, and lung cancer.

Biography
Dr. Anant Madabhushi is the Director of the Center for Computational Imaging and Personalized Diagnostics (CCIPD) and the F. Alex Nason Professor II in the Department of Biomedical Engineering, Case Western Reserve University. His team has developed pioneering computer aided diagnosis, pattern recognition, image analysis tools for diagnosis and prognosis of different types of cancers (prostate, breast, medulloblastoma, oropharyngeal) based on quantitative and computerized histomorphometric image analysis of digitized histologic biopsy tissue specimens. This novel approach involves quantitatively mining the histologic image data for hundreds of image features via sophisticated image segmentation, feature extraction, machine learning and pattern recognition methods and then predicting the risk of disease recurrence and patient prognosis. His group has also pioneered new ways of combining histomorphometric imaging features with “omics” derived biomarkers for improved and integrated prediction of cancer outcome. His team has published over 140 peer-reviewed journal papers and over 160 peer-reviewed conference papers (Google H-Index=46) and has over 70 patents awarded or pending.

30 May

31 May

Daniel Racoceanu

Professor in Biomedical Image and Data Computing
Pontifical Catholic University of Peru
Abstract: Breast carcinomas are cancers that arise from the epithelial cells of the breast, which are the cells that line the lobules and the lactiferous ducts. Breast carcinoma is the most common type of breast cancer and can be divided into dierent subtypes based on architectural features and growth patterns, recognized during a histopathological examination. Tumor microenvironment (TME) is the cellular environment in which tumor cells develop. Being composed of various cell types having dierent biological roles, TME is recognized as playing an important role in the progression of the disease. The architectural heterogeneity in breast carcinomas and the spatial
interactions with TME are not yet perfectly understood. Developing a spatial model of tumor architecture and spatial interactions with TME can advance our understanding of tumor heterogeneity. Furthermore, generating histological synthetic datasets can contribute to validating, and comparing analytical methods that are used in digital pathology. The model proposed here applies to dierent breast carcinoma subtypes and TME spatial distributions based on mathematical morphology. It is based on a few morphological parameters that give access to a large spectrum of breast tumor architectures and are able to dierentiate in-situ ductal carcinomas (DCIS) and histological subtypes of invasive carcinomas such as ductal (IDC) and lobular
carcinoma (ILC). In addition, a part of the parameters of the model controls the spatial distribution of TME relative to the tumor. The test of the model is performed by comparing morphological features between real and simulated images.
Biography:
Professor in Biomedical Image and Data Computing at the Pontifical Catholic University of Peru, I have a tenured Professor position at Sorbonne University, Paris. My areas of competency are Medical Image Analysis, Pattern Recognition, and Machine Learning, my research being mainly focused on Digital Pathology and its Integrative aspects. HDR (2006) and Ph.D. (1997) of the Univ. of Franche-Comté, Besançon, France, I was Project Manager at General Electric Energy Products - Europe, before joining, in 1999, a chair of Associate Professor at University of Besançon, Research Fellow at FEMTO-ST Institute (French National Research Center - CNRS), Besancon, France. From 2005 to 2014, I participated in the creation and the development of the International Joint Research Unit (UMI CNRS) Image & Pervasive Access Lab, being the Director (from 2008 to 2014) of this joint research venture created between Sorbonne University, the French National Center for Scientific Research (CNRS), the National University of Singapore (NUS), the Agency for Science, Technology and Research (A*STAR), the Univ. Grenoble Alpes and the Institut Mines-Telecom, in Singapore. From 2009 to 2015, I was Associate and then Full Professor (adjunct) at the School of Computing, National University of Singapore. Between 2014 and 2016, I was a member of the Executive Board of the University Institute of Health Engineering of the Sorbonne Université, being also co-Director and co-initiator of a new B.Sc. Minor, dedicated to Innovation in Public Health. During the same period, I was leading the Cancer Theranostics research team at the Bioimaging Lab, a joint research unit created between Sorbonne Université, CNRS and INSERM (French National Institute of Health and Medical Research). I am vice-President of the European Society for Digital Integrative Pathology (ESDIP) and member of MICCAI Board of Directors (Medical Image Computing & Computer Assisted Intervention).

30 May

01 June

01 June

Keith Kaplan

Publisher
Tissuepathology.com
Dr. Kaplan is board certified in anatomic and clinical pathology. His subspecialty interests include gastrointestinal and hepatic pathology, cytopathology and pathology informatics as well as research interests in gastrointestinal and hepatobiliary pathology, hyperspectral imaging, image analysis and the use of Web 2.0 tools in pathology. He has authored over 60 peer-reviewed scientific articles, book chapters, editorials and scientific abstracts and frequently lectures at both national and international meetings on topics related to pathology informatics. He is the founder and chief editor of the popular blog Tissuepathology.com and Dr. Kaplan will present reflections on recent advances in the field, from the bird’s eye view that the blog gives him

30 May

30 May

Yair Rivenson

Postdoctoral Fellow
University of California Los Angeles
Title
Beyond classification: deep learning for computational microscopy in digital pathology

Abstract

In recent years, deep learning has redefined the state of the art results for diagnosis and classification tasks in digital pathology. Here, we demonstrate the application of deep learning for enhancing microscopic imaging for digital pathology, with some unique challenges and opportunities that this framework brings. Amongst the applications, we will demonstrate enhancement of benchtop microscope images, extending their resolution, depth of field and field of view. Another application is the extension of this framework for mobile, smartphone-based, microscopy, where deep learning enables users to match the imaging performance of a laboratory grade benchtop microscope, with a cost-effective smartphone microscope. The deep learning essentially learns to eliminate spectral distortions, increase signal-to-noise ratio and enhance resolution, even for highly compressed images, that can be used in devices deployed in low resource settings areas. We’ll also discuss the application of deep learning for coherent imaging systems, recovering both amplitude and phase of an object, from a single diffraction pattern. Finally, we’ll demonstrate the application of virtual histopathology staining, where a deep network can learn how to digitally stain a single, label-free (unstained), autofluorescent image to match the same image of the tissue section as it would have been chemically stained (for example, using H&E or Masson’s Trichrome stains) and imaged using a brightfield microscope. All these results will be demonstrated on thin tissue sections as well as blood and Pap smears. These results establish the potential of deep learning as a promising framework for multimodal computational microscopy in digital pathology.

Biography

Yair Rivenson is a postdoctoral researcher in the Electrical and Computer Engineering, University of California, Los Angeles (UCLA). His research is aiming for the development of intelligent biomedical computational imaging and sensing platforms, specifically applying novel mathematical frameworks, such as deep learning, with numerical-physical systems modeling. Dr. Rivenson has recently focused his research on deep learning-based approaches, which can be used as a new framework for computational microscopy, significantly enhancing the performance of standard and mobile microscopic modalities.

30 May

Nick Haarselhorst

Professional Services Consultant
Philips
Title: Digital Pathology in the landscape of Interoperability, Regulations & Standards

Biography
Nick Haarselhorst, Professional Service Consultant & Interoperability Architect within the Service Innovation group of Philips Digital Pathology Solutions. Within this role he provides technical leadership in respect to interoperability and integration within the DP projects and be responsible for the final solution design to be provided. Beside that he contributed to many standardization efforts and represented Philips Healthcare in standardization bodies like IHE (Europe, Services, The Netherlands, and PaLM), DICOM and HL7.
Nick Haarselhorst will give an overview of the actual status of interoperability and the way forward in the coming years from a vendor perspective.

31 May

David de Mena García

Innovation Project Manager
Andalusian Public Health System
Title: New European medical device regulation. Impact on slide scanners and image analysis solutions

Biography
He has graduated as a Telecommunications Engineer from the University of Seville, Bachelour in Philosophy from Laternanese University of Rome, MBA from the School of Industrial Organization and currently holds a doctorate in Applied Sciences from the University of Castilla la Mancha in DICOM and Digital Pathology Images. He has several specializations in Digital Health and Entrepreneurship by international universities and business schools. Member of DICOM WG26 and IHE PaLM group and ISO 13485 Internal auditor. He has collaborate as invited speaker at University of Seville as well as different digital pathology schools. As Innovation Projects Manager, he has been developing his professional career in the central node of the Deputy Direction of ICT in the Andalusian Public Health System, advising and preparing innovation projects in Information Technologies and Communications. He has participated in several national and European projects.

31 May

31 May

Clare Verrill

Associate Professor
Oxford University
Title
Deep learning for detecting tumour infiltrating lymphocytes in testicular germ cell tumours

Abstract
Machine learning and deep learning in particular hasshown potential for expert-level accuracy in biomedical image classification. Through collaboration between Oxford University and the Finnish Institute of Molecular Medicine, a deep learning network was developed to identify and count tumour infiltrating lymphocytes (TILs) in testicular germ cell tumours as well as to predict disease relapse. Digitized haematoxylin-eosin (H&E) stained tumour whole slides from 89 patients with clinicopathological data were evaluated. Patients without testicular cancer relapse in general had higher TIL density in the primary tumour compared to patients who relapsed and in seminomas none of the relapsed cases belonged to the highest TIL density tertile (>2011/mm2, P=0.04, Fisher’s exact test). TIL quantifications performed visually by three pathologists on the same tumours were not significantly associated with outcome. The average inter-observer agreement between the pathologists when assigning a patient into TIL tertiles was 0.32 (Kappa test), compared to 0.35 between the algorithm and the experts respectively. A higher TIL density was associated with a lower clinical tumour stage, seminoma histology and lack of lymphovascular invasion at presentation.We show that deep learning-based image analysis can be applied to automated detection of TILs in H&E stained digitized samples of testicular germ cell cancer and that it has potential for use as a prognostic marker for disease relapse.

Biography
Clare Verrill, FRCPath is an associate professor of pathology with the Nuffield Department of Surgical Sciences at the University of Oxford, UK. She works partly as a diagnostic urological pathologist and partly as an academic pathologist and has her own research group. She is the UK National Cancer Research Institute Cellular Molecular Pathology (CM-Path) Initiative Workstream lead for Technology and Informatics. Her research interests include digital pathology applications within urological pathology, in particular in prostate and testis. She will give an overview of a collaborative project between Oxford University and the Finnish Institute of Molecular Medicine in which a deep learning tool was developed to quantify tumour infiltrating lymphocytes in testicular germ cell tumours.

31 May

JR

Juan A. Retamero

Pathologist
Granada University Hospital

01 June

LC

Luca Cima

University and Hospital Trust Of Verona

01 June

SF

Shereen Fouad

University of Birmingham

30 May

DM

Diana Mandache

Institut Pasteur

30 May

Vincenzo Della Mea

University Of Udine

30 May

30 May

DP

David Pilutti

University of Udine

30 May

RA

Ruqayya Awan

University of Warwick

01 June

KK

Kimmo Kartasalo

University of Tampere

01 June

AJ

Andrew Janowczyk

Case Western Reserve University

01 June

RM

Raphaël Marée

University Of Liège

30 May

SB

Sami Blom

Fimmic
Biography
Sami Blom is the application manager at Fimmic Oy. He has a background in the fields of translational cancer research and in vitro diagnostics, and has specialized in the development of tissue staining and imaging methods for solid tumours. Sami holds a M.Sc. degree in biochemistry from the University of Turku, Finland.

30 May

ME

Micha Eichmann

University of Bern

30 May

OH

Oscar Holmström

FIMM, University of Helsinki

30 May

KS

Kevin Sandeman

University of Helsinki

31 May

Teijo Pellinen

FIMM, University of Helsinki

01 June

CL

Cecilia Lindskog

Uppsala University

31 May

David Ameisen

Université Paris Descartes

31 May

CF

Carlos Fernandez Moro

Karolinska Institutet

30 May