Workshop on Computational Challenges in Digital Pathology

(TBA) October 2021 | Online event

About the Workshop

Recently, Deep Learning has produced rapid advances in the performance of medical image analysis challenging physicians in their traditional field. In pathology in particular, automated procedures can help to reduce the pathologists' workload and increase the accuracy and precision of pathology assessments, which are often considered subjective and with less than optimal reproducibility. In addition, Deep Learning and Computer Vision demonstrate the ability/potential to extract more clinically relevant information from whole slide images, compared to what is possible in current routine clinical practise by human assessors. Nevertheless, considerable development and validation work lies ahead before AI-based methods are ready for integration into pathology departments.

The size of whole-slide image data, the weak annotation of specimens, the variability in slide preparation due to different procedures (e.g., staining, digitization) make the learning environment a major challenge, even for state-of-the-art algorithms, requiring further contributions from the Computer Vision community. Moreover, the need for transparent decision making further increases the challenge.

The workshop on Computational Challenges in Digital Pathology (CDpath) at ICCV 2021 aims to foster discussion and presentation of ideas to tackle these challenges and identify research opportunities in the context of Computational Pathology.

Keynote Speakers

Opening Session

Anne Martel

Keynote Talk: Prof. Anne Martel

TBA (35 min presentation + 10 min Q&A)

Oral Session I: set of accepted manuscripts

(10/15 min presentation + 5 min Q&A)

Coffee Break

Martin Stumpe

Keynote Talk: Dr. Martin Stumpe

TBA (35 min presentation + 10 min Q&A)

Oral Session II: set of accepted manuscripts

(10/15 min presentation + 5 min Q&A)

Closing Session

* All times are PST - Pacific Standard Time *

Paper submission

The CDpath workshop aims at providing a platform for scientific discussion on computational pathology, introducing the challenges of the field to the Computer Vision and Artificial Intelligence community.

The CDpath workshop welcomes works that focus on the computational pathology challenges, such as:

Semi/unsupervised learning methodologies in computational pathology;
Detection, classification and segmentation of tissue structures;
Disease diagnosis, grading and prognosis;
Treatment response prediction;
Staining quality assessment and standardization;
Detection of tissue biomarkers with predictive/prognostic value;
Whole-slide image registration;
Explainable AI for computational pathology;
Scalable techniques for whole-slide image processing;
Applications for computational pathology in the clinic.

Authors should prepare a manuscript of no more than 8 pages, including images and tables (with possible extra pages containing only cited references). The manuscript submitted to the workshop should be formatted according to the ICCV style and anonymized. Papers that are not properly anonymized, or do not use the template, or have more than 8 pages, excluding references, will be desk-rejected.

All submissions will be reviewed by 3 reviewers, in a double-blind reviewing process. Authors/reviewers will be asked to disclose any potential conflicts of interest, such as collaborations in the last 3 years. The selection of the papers will be based on their relevance for the computational pathology topic, technical and experimental quality, significance of results, and clear presentation.

The workshop accepted papers will be published in IEEE Xplore in conjunction with ICCV 2021 proceedings.

Important dates

Submission deadline: July 25, 2021*
Author notification: August 11, 2021*
Camera ready deadline: August 17, 2021*
ICCV 2021 conference: October 11-17, 2021
CDpath workshop: (TBA) October, 2021
*All deadlines are at 23:59 PST

General Chairs
Program Chairs
Publicity Chairs
Sponsorship Chairs
Technical Program Committee

Antonio Foncubierta Rodriguez, Research Staff Member at IBM, Switzerland
Andreas Holzinger, Head of HCAI Lab at Med Uni Graz & Visiting Professor at TU Wien/Amii, Austria
April Khademi, Principal Investigator of IAMLAB & Assistant Professor at Ryerson University, Canada
Azam Hamidinekoo, Postdoctoral Training Fellow at Institute of Cancer Research, UK
Cheng Lu, Research Assistant Professor at Case Western Reserve University, USA
Faisal Mahmood, Head of Mahmood Lab & Assistant Professor at Harvard Medical School, USA
Francesco Ciompi, Assistant Professor at Radboud University Medical Center, The Netherlands
Geert Litjens, Assistant Professor at Radboud University Medical Center, The Netherlands
Hamid Tizhoosh, Head of Kimia Lab & Professor at University of Waterloo, Canada
Heather Couture, ML Consultant/Researcher/Owner at Pixel Scientia Labs, USA
Henning Müller, Head of MedGIFT Lab & Professor at HES-SO Valais-Wallis & University of Geneva, Switzerland
Jana Lipkova, Postdoctoral Researcher at Harvard Medical School, USA
Lee Cooper, Computational Pathology Director/Associate Professor at Northwestern University, USA
Manfredo Atzori, Senior Researcher at HES-SO Valais-Wallis and Assistant Professor at UNIPD, Switzerland
Maschenka Balkenhol, Pathology Resident/Researcher at Radboud University Medical Center, The Netherlands
Matthew Lee, AI Scientist at Paige.AI, UK
Mitko Veta, Assistant Professor at Eindhoven University of Technology, The Netherlands
Nasir Rajpoot, Founding Director of TIA Centre & co-Director of PathLAKE & Professor at Warwick University, UK
Navid Alemi, AI Scientist at Paige.AI, UK
Peter Bankhead, Senior Lecturer in Digital Pathology at the University of Edinburgh, Scotland
Peter Schüffler, Assistant Professor at TUM & Co-founder/Senior AI Scientist at Paige.AI, Germany
Saad Ullah Akram, MVision AI Co-Founder/CTO & Visiting Postdoctoral Researcher at Aalto University, Finland
Talha Qaiser, Researcher at Imperial College London & Senior Scientist at AstraZeneca, UK
Yukako Yagi, Head of The Yukako Yagi Lab & Director of Pathology Digital Imaging at MSKC Center, USA
Yun Liu, Staff Research Scientist at Google Health, USA