Program

8:30 – 9:20 Registration


9:20 – 9:30 Opening


9:30 – 11:10 Oral session I: Object detection and tissue classification

 9:30 – 10:00Invited talk: Data variability as a Challenge to improve classification and Retrieval in Digital Pathology

Dr. Manfredo Atzori, University of Applied Sciences Western Switzerland

 10:00 - 10:20Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection

Saad Ullah Akram*, University of Oulu, Finland; Talha Qaiser, University of Warwick; Simon Graham, University of Warwick; Juho Kannala, Aalto University, Finland; Janne Heikkila, University of Oulu, Finland; Nasir Rajpoot, University of Warwick

 10:20 - 10:40Automatic Detection of Tumor Budding in Colorectal Carcinoma with Deep Learning

John-Melle Bokhorst*, Radboud University Medical Center; Francesco Ciompi, Radboud University Medcial Center; Jeroen van der Laak, Radboud University Medical Center; Iris Nagtegaal, Radboud UMC; Lucia Rijstenberg, RadboudUMC; Danny Goudkade, Maastricht University Medical Center

 10:40 - 11:00Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network

Yongxiang HUANG*, The Hong Kong University of Science and Technology; Albert Chung, Hong Kong University of Science and Technology

 11:00 - 11:10 Software demo pitches

11:10 – 11:30 Coffee break


11:30 – 13:30 Oral session II: Tissue segmentation

 11:30 – 12:00Invited talk: AI as an Integrated Tool into a Fully Digital Routine Workflow in a Pathology Department

Dr. Filippo Fraggetta, Cannizzaro Hospital, Italy

 12:00 – 12:20Cellular Community Detection for Tissue Classification

Sajid Javed*, University of Warwick; Muhammad Moazam Fraz, University of Warwick; David Epstein, University of Warwick; David Snead, University Hospitals Coventry & Warwickshire NHS Trust; Nasir Rajpoot, University of Warwick

 12:20 – 12:40Role of Task Complexity and Training in Crowdsourced Image Annotation

Nadine Schaadt*, Hannover Medical School; Anne Grote, Hannover Medical School; Germain Forestier, University of Haute Alsace; Cédric Wemmert, University of Strasbourg; Friedrich Feuerhake, Hannover Medical School

 12:40 – 13:00DeepCerv: Deep Neural Network for Segmentation Free Robust Cervical Cell Classification

Nirmal Jith O U*, AIndra Systems; Harinarayanan K K, Aindra Systems; Srishti Gautam, Indian Institute of Technology Mandi; Arnav Bhavsar, IIT Mandi; Anil Kumar Sao, IIT Mandi

 13:00 – 13:20Uncertainty Aware Deep Neural Network for Microvessels Segmentation in H&E Stained Histology Images

Muhammad Moazam Fraz*, University of Warwick; Muhammad Shaban, University of Warwick; Simon Graham, University of Warwick; Syed Ali khurram, University of Sheffield; Nasir Rajpoot, University of Warwick

 13:20 - 13:30 Poster pitches


13:30 – 15:00 Poster and Software Demo session I / Lunch

Software demos

PathoVA
A Visual Analytics Tool for Pathology Diagnosis and Reporting


Alberto Corvò, Eindhoven University of Technology, Netherlands
ASAP
Automated Slide Analysis Platform


Geert Litjens, Radboud University Medical Center, Netherlands


Posters

Multi-Resolution Networks for Semantic Segmentation in Whole Slide Images

Feng Gu*, ContextVision AB; Nikolay Burlutskiy, ContextVision AB; Mats Andersson, ContextVision AB; Lena Kajland Wilen, ContextVision AB

Evaluating Out-of-the-box Methods for the Classification of Haematopoietic Cells in Images of Stained Bone Marrow

Philipp Gräbel*, Lehrstuhl für Bildverarbeitung, RWTH Aachen; Barbara Klinkhammer, RWTH Aachen University; Dorit Merhof, RWTH Aachen University

Improving Accuracy of Nuclei Segmentation by Reducing Histological Image Variability

Yusuf Roohani*, Glaxosmithkline; Eric Kiss, Stanford University

Exploiting Multiple Color Representations to Improve Colon Cancer Detection in Whole Slide H&E Stains

Alex Jørgensen*, Aalborg University; Jonas Emborg, Diagnostics & Genomics Group, Dako Denmark A/S, an Agilent Technologies Company; Rasmus Røge, Institute of Pathology, Aalborg University Hospital, Denmark, and the Department of Clinical Medicine, Aalborg University, Denmark; Lasse Østergaard, Aalborg University

Image Magnification Regression Using DenseNet for Exploiting Histopathology Open Access Content

Juan Otálora Montenegro*, HES-SO; Vincent Andrearczyk, HES-SO Valais; Manfredo Atzori, University of Applied Sciences Western Switzerland (HES-SO Valais); Henning Müller, Western Switzerland Sierre



15:00 – 16:40 Oral session III: Registration and 3D histology imaging

 15:00 – 15:30Invited talk: Novel Problems and Approaches for Volumetric 3D Pathology

Mattew Goodman, 3Scan

 15:30 – 15:50Modality Conversion from Pathological Image to Ultrasonic Image Using Convolutional Neural Network

Takashi Ohnishi*, Chiba University; Shu Kashio, Chiba University; Takuya Ogawa, Chiba University; Kazuyo Ito, Chiba University; Stanislav S. Makhanov, School of Information and Computer Technology, Sirindhorn International Institute of Technology, Thammasat University, Thailand; Tadashi Yamaguchi, Chiba University; Yasuo Iwadate, Chiba University; Hideaki Haneishi, Chiba University

 15:50 – 16:10Accurate 3D Reconstruction of a Whole Pancreatic Cancer Tumor from Pathology Images with Different Stains

Mauricio Kugler*, Nagoya Institute of Technology; Yushi Goto, Nagoya Institute of Technology; Naoki Kawamura, Nagoya Institute of Technology; Hirokazu Kobayashi, Nagoya Institute of Technology; Tatsuya Yokota, Nagoya Institute of Technology; Chika Iwamoto, Kyushu University; Kenoki Ohuchida, Kyushu University; Makoto Hashizume, Kyushu University; Hidekata Hontani, Nagoya Institute of Technology

 16:10 – 16:30Whole Slide Image Registration for the Study of Tumor Heterogeneity

Leslie Solorzano*, Uppsala University; Carolina Wählby, Uppsala University; Carla Oliveira, Universidad do Porto

 16:30 - 16:40Poster pitches

16:40 – 17:00 Coffee break


17:00 – 18:30 Poster and Software Demo session II


Software demos

HistoQC
Quality control tool for digital pathology slides


Andrew Janowczyk, Case Western Reserve University, United States
Prateek Prasanna, Case Western Reserve University, United States
Nathaniel Braman, Case Western Reserve University, United States
Deep Learning based retrieval for gigapixel histopathology cases and open access literature

Sebastian Otálora, University of Applied Sciences Western Switzerland, Sierre (HES-SO)


Posters

Structure Instance Segmentation in Renal Tissue: a Case Study on Tubular Immune Cell Detection

Thomas de Bel*, Radboudumc; Jeroen van der Laak, Radboud University Medical Center; Geert Litjens, Radboud Univ. Medical Ctr.; Meyke Hermsen, Radboud University Medical Center

Capturing Global Spatial Context for Accurate Cell Classification in Skin Cancer Histology

Konstantinos Zormpas-Petridis*, The Institute of Cancer Research (ICR), London; Henrik Failmezger, The Institute of Cancer Research (ICR), London; Ioannis Roxanis, Royal Free London, NHS; Matthew Blackledge, The Institute of Cancer Research (ICR), London; Yann Jamin, The Institute of Cancer Research (ICR), London; Yinyin Yuan, Insititute for Cancer Research

Significance of Hyperparameter Optimization for Histology Images: A Study for Breast Metastasis Detection

Navid Alemi Koohbanani*, University of Warwick; Talha Qaiser, University of Warwick; Muhammad Shaban, University of Warwick; Nasir Rajpoot, University of Warwick

Construction of a Generative Model of H&E Stained Pathology Images of Pancreas Tumors Conditioned by a Voxel Value of MRI Image

Tomoshige Shimomura*, Nagoya Institute of Technology; Kugler Mauricio, Nagoya Institute of Technology; Tatsuya Yokota, Nagoya Institute of Technology; Chika Iwamoto, Kyushu University; Kenoki Ohuchida, Kyushu University; Makoto Hashizume, Kyushu University, Japan; Hidekata Hontani, Nagoya Institute of Technology


18:30 – 18:45 Closing remarks and best paper award