{"id":301,"date":"2021-09-07T12:26:35","date_gmt":"2021-09-07T12:26:35","guid":{"rendered":"https:\/\/www.examode.eu\/compay2021\/?page_id=301"},"modified":"2025-03-25T21:33:07","modified_gmt":"2025-03-25T21:33:07","slug":"program-2","status":"publish","type":"page","link":"https:\/\/www.examode.eu\/compay2021\/program-2\/","title":{"rendered":"Program"},"content":{"rendered":"\n
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Feel free to send questions to the speakers via the following links: oral session questions<\/a>, panel discussion questions<\/a><\/p>\n\n\n\n

The full proceedings of COMPAY 2021 are available as Proceedings of Machine Learning Research<\/a><\/p>\n<\/div>\n<\/div>\n\n\n\n

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Time zone: UCT<\/p>\n<\/div>\n<\/div>\n\n\n\n

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9:00<\/td>Opening<\/td><\/td><\/tr>
 <\/td>SESSION 1<\/strong> – Chair: Henning M\u00fcller<\/td> <\/td><\/tr>
9:10<\/td>Keynote presentation<\/span><\/td><\/td><\/tr>
<\/td>AI in pathology: promise and potential<\/strong>
Monika Lamba Saini (CellCarta, Belgium)<\/em> \u00a0<\/em>


<\/td>		<\/td><\/tr>
9:40<\/td>Oral presentations<\/span><\/td><\/td><\/tr>
<\/td>Automated Quantification Of Blood Microvessels In Hematoxylin And Eosin Whole Slide Images<\/a><\/strong>
Azam Hamidinekoo, Anna Kelsey, Nicholas Trahearn, Joanna Selfe, Janet Shipley, Yinyin Yuan<\/em>

HistoCartography: A Toolkit for Graph Analytics in Digital Pathology<\/a><\/strong>
Guillaume Jaume, Pushpak Pati, Valentin Anklin, Antonio Foncubierta, Maria Gabrani<\/em>

Detecting genetic alterations in BRAF and NTRK as oncogenic drivers in digital pathology images: towards model generalization within and across multiple thyroid cohorts<\/a><\/strong>
Johannes H\u00f6hne, Jacob de Zoete, Arndt A Schmitz, Tricia Bal, Emmanuelle di Tomaso, Matthias Lenga<\/em><\/td>		<\/td><\/tr>
10:10<\/td>Q\/A session oral presentations<\/span><\/td><\/td><\/tr>
10:25<\/td>Software demo pitches<\/span><\/td><\/td><\/tr>
<\/td>A practical guide to using HookNet<\/a><\/strong>
Mart van Rijthoven <\/em> 

Stainlib: a python library for augmentation and normalization of histopathology H&E images<\/a><\/strong>
Niccol\u00f2 Marini <\/em>

Multi_Scale_Tools: a python library to exploit multi-scale whole slide images<\/a><\/strong>
Niccol\u00f2 Marini<\/em>

QuickAnnotator <\/a><\/strong>
Andrew Janowczyk<\/em>
<\/td>		<\/td><\/tr>
 10:35<\/td>Poster & software demo session #1<\/span><\/td> <\/td><\/tr>
<\/td>Posters<\/span>
Autom<\/a>ated Quantification Of Blood Microvessels In Hematoxylin And Eosin Whole Slide Images<\/a><\/strong>
Azam Hamidinekoo, Anna Kelsey, Nicholas Trahearn, Joanna Selfe, Janet Shipley, Yinyin Yuan<\/em>
 <\/em>
HistoCartograph<\/a>y<\/a>: A Toolkit for Graph Analytics in Digital Pathology<\/strong>
Guillaume Jaume, Pushpak Pati, Valentin Anklin, Antonio Foncubierta, Maria Gabrani<\/em>
 <\/em>
Detecting geneti<\/a>c alterations in BRAF and NTRK as oncogenic drivers in digital pathology images: towards model generalization within and across multiple thyroid cohorts<\/a><\/strong>
Johannes H\u00f6hne, Jacob de Zoete, Arndt A Schmitz, Tricia Bal, Emmanuelle di Tomaso, Matthias Lenga<\/em>
 
End-to-end Multiple Instance Learning for Whole-Slide Cytopathology of Urothelial Carcinoma<\/a><\/strong>
Joshua Butke, Tatjana Frick, Florian Roghmann, Samir El-Mashtoly, Klaus Gerwert, Axel Mosig<\/em>
 
Attention-based Multiple Instance Learning with Mixed Supervision on the Camelyon16 Dataset<\/a><\/strong>
Paul Tourniaire, Nicholas Ayache, Herv\u00e9 Delingette<\/em>
 
A Multi-scale Graph Network with Multi-head Attention for Histopathology Image Diagnosis<\/a><\/strong>
Xiaodan Xing, Yixin Ma, Lei Jin, Tianyang Sun, Zhong Xue, Feng Shi, Jinsong Wu, Dinggang Shen<\/em>
 
Unsupervised Domain Adaptation for the Histopathological Cell Segmentation through Self-Ensembling<\/a><\/strong>
Chaoqun Li, Yitian Zhou, Tangqi Shi, Yenan Wu, Meng Yang, Zhongyu Li<\/em>
 
Creating small but meaningful representations of digital pathology images<\/a><\/strong>
Corentin Guerendel, Phil Arnold, Benjamin Torben-Nielsen<\/em>
 
Molecular Subtype Prediction for Breast Cancer Using H&E Specialized Backbone<\/a><\/strong>
Samaneh Abbasi Sureshjani, Anil Yuce, Simon Till Sch\u00f6nenberger, Maris Skujevskis, Uwe Schalles, Fabien Gaire, Konstanty Korski<\/em>
 
SMILE: Sparse-Attention based Multiple Instance Contrastive Learning for Glioma Sub-type Classification Using Pathological Image<\/a><\/strong>
Mengkang Lu, Yongsheng Pan, Dong Nie, Feng Shi, Feihong Liu, Yong Xia, Dinggang Shen<\/em>
 
Software demos<\/span>
 A practical guide to using HookNet<\/a><\/strong>
Mart van Rijthoven<\/em>
 
Stainlib: a python library for augmentation and normalization of histopathology H&E images<\/strong>
Multi_Scale_Tools: a python library to exploit multi-scale whole slide images<\/a><\/strong>
Niccol\u00f2 Marini<\/em><\/td>		<\/td><\/tr>
 <\/td><\/td> <\/td><\/tr>
 <\/td>SESSION 2<\/strong> – Chair: Jeroen van der Laak<\/td> <\/td><\/tr>
11:45<\/td>Keynote presentatio<\/span>n<\/td><\/td><\/tr>
<\/td>Taking AI to the next level with Data Centric approach in Computational Pathology<\/strong>
Chen Sagiv (DeePathology.ai, Israel)<\/em><\/td>		<\/td><\/tr>
12:15<\/td>Oral presentations<\/span><\/td><\/td><\/tr>
<\/td>SparseConvMIL: Sparse Convolutional Context-Aware Multiple Instance Learning for Whole Slide Image Classification<\/a><\/strong>
Marvin Lerousseau, Maria Vakalopoulou, Eric Deutsch, Nikos Paragios<\/em>
 <\/em>
Multi-scale Regional Attention Deeplab3+: Multiple Myeloma Plasma Cells Segmentation in Microscopic Images<\/strong>
Afshin Bozorgpour, Reza Azad, Eman Showkatian, Alaa Sulaiman<\/em><\/a>
 <\/em>
Symmetric Dense Inception Network for Simultaneous Cell Detection and Classification in Multiplex Immunohistochemistry Images<\/a><\/strong>
Hanyun Zhang, Tami Grunewald, Ayse U. Akarca, Teresa Marafioti, Jonathan A. Ledermann, Yinyin Yuan<\/em><\/td>		<\/td><\/tr>
12:45<\/td>Q\/A session oral presentations<\/span><\/td><\/td><\/tr>
13:00<\/td>Lunch break<\/span><\/td><\/td><\/tr>
 <\/td> <\/td> <\/td><\/tr>
 <\/td>SESSION 3<\/strong> – Chair: Mitko Veta<\/td> <\/td><\/tr>
14:00<\/td>Keynote presentation<\/span><\/td><\/td><\/tr>
<\/td>Data-efficient and multimodal computational pathology<\/strong>
Faisal Mahmood (Harvard Medical School)  <\/em><\/td>		<\/td><\/tr>
14:30<\/td>Oral presentations<\/span><\/td><\/td><\/tr>
<\/td>Self supervised learning improves dMMR\/MSI detectio<\/a>n from histology slides across multiple cancers<\/a><\/strong>
Charlie Saillard, Olivier Dehaene, Tanguy Marchand, Olivier Moindrot, Aur\u00e9lie Kamoun, Benoit Schmauch, Simon Jegou<\/em>
 
Automatic and explainable grading of meningiomas from histopathology images<\/a><\/strong>
Jonathan Ganz, Tobias Kirsch, Christof Albert Bertram, Christoph Hoffmann, Andreas Maier, Katharina Breininger, Ingmar Bl\u00fcmcke, Samir Jabari, Marc Aubreville<\/em>
 
Robust quad-tree based registration of whole slide images<\/a><\/strong>
Christian Marzahl, Frauke Wilm, Christine Kr\u00f6ger, Franz F Dressler, Lars Tharun, Sven Perner, Christof Bertram, J\u00f6rn Voigt, Robert Klopfleisch, Andreas Maier, Marc Aubreville, Katharina Breininger<\/em>  <\/td>		<\/td><\/tr>
 15:15<\/td>Poster and software demo session #2<\/span><\/td> <\/td><\/tr>
<\/td>Posters
<\/span>SparseConvMIL<\/a>: Sparse Convolutional Context-Aware Multiple Instance Learning for Whole Slide Image Classification<\/a><\/strong>
Marvin Lerousseau, Maria Vakalopoulou, Eric Deutsch, Nikos Paragios<\/em>
 <\/em>
Multi-scale Regional Attention Deeplab3+: Multiple Myeloma Plasma Cells Segmentation in Microscopic Images<\/a><\/strong>
Afshin Bozorgpour, Reza Azad, Eman Showkatian, Alaa Sulaiman<\/em>
 <\/em>
Symmetric Dense Inception Network for Simultaneous Cell Detection and Classification in Multiplex Immunohistochemistry Images<\/a><\/strong>
Hanyun Zhang, Tami Grunewald, Ayse U. Akarca, Teresa Marafioti, Jonathan A. Ledermann, Yinyin Yuan<\/em>
 <\/em>
Self supervised learning improves dMMR\/MSI detectio<\/a>n from histology slides across multiple cancers<\/a><\/strong><\/strong>
Charlie Saillard, Olivier Dehaene, Tanguy Marchand, Olivier Moindrot, Aur\u00e9lie Kamoun, Benoit Schmauch, Simon Jegou<\/em>
 
Automatic and explainable grading of meningiomas from histopathology images<\/a><\/strong><\/strong>
Jonathan Ganz, Tobias Kirsch, Christof Albert Bertram, Christoph Hoffmann, Andreas Maier, Katharina Breininger, Ingmar Bl\u00fcmcke, Samir Jabari, Marc Aubreville<\/em>
 
Robust quad-tree based registration of whole slide images<\/a><\/strong><\/strong>
Christian Marzahl, Frauke Wilm, Christine Kr\u00f6ger, Franz F Dressler, Lars Tharun, Sven Perner, Christof Bertram, J\u00f6rn Voigt, Robert Klopfleisch, Andreas Maier, Marc Aubreville, Katharina Breininger<\/em>
 
Multi-Scale Task Multiple Instance Learning for the Classification of Digital Pathology Images with Global Annotations<\/a><\/strong>
Niccol\u00f2 Marini, Sebastian Ot\u00e1lora, Francesco Ciompi, Gianmaria Silvello, Stefano Marchesin, Simona Vatrano, Gianziana Buttafuoco, Manfredo Atzori, Henning M\u00fcller<\/em>
 
Improving Mask R-CNN for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Images<\/a><\/strong>
Benjamin Bancher, Amirreza Mahbod, Isabella Ellinger, Rupert Ecker, Georg Dorffner<\/em>
 
Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging<\/a><\/strong>
Shunxing Bao, Yucheng Tang, Ho Hin Lee, Riqiang Gao, Sophie Chiron, Ilwoo Lyu, Lori A. Coburn, Keith T. Wilson, Joseph T. Roland, Bennett A. Landman, Yuankai Huo<\/em>
 
An Automatic Nuclei Image Segmentation Based on Multi-Scale Split-Attention U-Net<\/a><\/strong>
Qing Xu, Wenting Duan<\/em>
 
Magnetic Resonance Imaging Virtual Histopathology from Weakly Paired Data<\/a><\/strong>
Amaury Leroy, Kumar Shreshtha, Marvin Lerousseau, Th\u00e9ophraste Henry, Th\u00e9o ESTIENNE, Marion Classe, Nikos Paragios, Vincent Gr\u00e9goire, Eric Deutsch<\/em>
 
Deep Learning for interpretable end-to-end survival prediction in gastrointestinal cancer histopathology<\/a><\/strong>
Narmin Ghaffari Laleh, Amelie Echle, Hannah Sophie Muti, Katherine Jane Hewitt, Volkmar Schulz, Jakob Nikolas Kather<\/em>
 
A Novel Cell Map Representation for Weakly Supervised Prediction of ER and PR Status from H&E WSIs<\/a><\/strong>
Hammam Alghamdi, Navid Alemi Koohbanani, Nasir Rajpoot, SHAN E AHMED RAZA<\/em>
 
Software demos<\/span>
HistoQC<\/a><\/strong>
QuickAnnotator<\/a><\/strong>
PatchSorter<\/strong>
Andrew Janowczyk<\/em><\/td>		<\/td><\/tr>
 16:45<\/td>PANEL DISCUSSION<\/strong> – Chairs: Nasir Rajpoot, Francesco Ciompi<\/td> <\/td><\/tr>
<\/td>Invited panelists:
– Katherine Elfer (FDA)
– Chen Sagiv (DeePathology.ai)
– Roberto Salgado (TIL Working Group)
– Inti Zlobec (University of Bern)
– Faisal Mahmood (Harvard Medical School)
– Monika Lamba Saini (CellCarta)<\/td>		<\/td><\/tr>
17:45
17:55<\/td>		Organizing team short presentations
Awards & closing<\/td>		<\/td><\/tr><\/tbody><\/table>
Sponsored by \"\"<\/a><\/figcaption><\/figure>\n","protected":false},"excerpt":{"rendered":"

Feel free to send questions to the speakers via the following links: oral session questions, panel discussion questions The full proceedings of COMPAY 2021 are available as Proceedings of Machine Learning Research Time zone: UCT 9:00 Opening   SESSION 1 – Chair: Henning M\u00fcller   9:10 Keynote presentation AI in pathology: promise and potential Monika… Continue reading Program<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-301","page","type-page","status-publish","hentry","entry"],"_links":{"self":[{"href":"https:\/\/www.examode.eu\/compay2021\/wp-json\/wp\/v2\/pages\/301","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.examode.eu\/compay2021\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.examode.eu\/compay2021\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.examode.eu\/compay2021\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.examode.eu\/compay2021\/wp-json\/wp\/v2\/comments?post=301"}],"version-history":[{"count":66,"href":"https:\/\/www.examode.eu\/compay2021\/wp-json\/wp\/v2\/pages\/301\/revisions"}],"predecessor-version":[{"id":432,"href":"https:\/\/www.examode.eu\/compay2021\/wp-json\/wp\/v2\/pages\/301\/revisions\/432"}],"wp:attachment":[{"href":"https:\/\/www.examode.eu\/compay2021\/wp-json\/wp\/v2\/media?parent=301"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}