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Papers, Thoughts and Research Insights (PaTaRI)

Together with Prof. David Epstein, I run a journal club called PaTaRI (lit. meaning hamper or basket often associated with surprises) as part of our Tissue Image Analytics centre. The objective of this journal club is to critique and analyze the latest papers as well as encourage a creative discussion on interesting topics to provide research insights and ideas for further exploration. Below is a list of papers/topics we have discussed in our sessions so far.

Paper Title Date Presenters

XIV Histological Characterization Techniques

8 Dec 2021

Fayyaz, Mark, Adam and Dawood.

XIII Towards Robust omputational Pathology

29 Sep 2021

Alex, Amina, Fayyaz

XII Surviving Survival Analysis-III: Introduction to Machine Learning Methods for Survival Analysis and Prediction

28 May 2021

Rob, Amina, Noor, Fayyaz

XI Surviving Survival Analysis-II: Introduction to Machine Learning Methods for Survival Analysis and Prediction

23 May 2021

David, Adam, Noor, Rob, Amina, Wenqi, and Fayyaz

Shan, Nasir

X Surviving Survival Analysis-I: Introduction to Statistical Methods for Survival Analysis and Prediction

19 May 2021

David, Adam, Noor, Rob, Amina, Wenqi, and Fayyaz

Shan, Nasir

Dense, high-resolution mapping of cells and tissues from pathology images for the interpretable prediction of molecular phenotypes in cancer by J. A. Diao et al.

24 Feb 2021

Noor, Simon and Rob
Warwick-NVIDIA Knowledge Exchange (Session-II)

13 Jan


Jonny Hancox, NVIDIA
Warwick-NVIDIA Knowledge Exchange (Session-I)

6 Jan


Jonny Hancox, NVIDIA
Lu, Cheng, Can Koyuncu, German Corredor, Prateek Prasanna, Patrick Leo, XiangXue Wang, Andrew Janowczyk, et al. “Feature-Driven Local Cell Graph (FLocK): New Computational Pathology-Based Descriptors for Prognosis of Lung Cancer and HPV Status of Oropharyngeal Cancers.” Medical Image Analysis, November 16, 2020, 101903.
25 Nov 2020 Simon, Wenqi, Noor, Adam, Hammam & Fayyaz

MICCAI 2020 CPATH papers

Censoring-Aware Deep Ordinal Regression for Survival Prediction from Pathological Images
Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer
Ranking Based Survival Prediction on Histopathological Whole Slide Images
Graph Attention Multi-instance Learning for Accurate Colorectal Cancer Staging
Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images
Modelling Histological Patterns for Differential Diagnosis of Atypical Breast Lesions
28 Oct 2020

Saad, Srijay, Ruqayya, Adam Navid, Dang, Dawood

Coord by Saad & Srijay

Pan-Cancer Computational Histopathology Reveals Mutations, Tumor Composition and Prognosis.” Nature Cancer 1, no. 8 (August 2020): 800–810. 30 Sep 2020 Nasir, Nima, Mohsin, Amina, Ali, Fayyaz, and David
Lu, Ming Y., Melissa Zhao, Maha Shady, Jana Lipkova, Tiffany Y. Chen, Drew F. K. Williamson, and Faisal Mahmood. “Deep Learning-Based Computational Pathology Predicts Origins for Cancers of Unknown Primary.” ArXiv:2006.13932 [Cs, q-Bio], June 28, 2020. 17 Jul 2020

Ayesha, Fayyaz, Jev, Shaban, Rawan

AbdulJabbar, Khalid, Shan E. Ahmed Raza, Rachel Rosenthal, Mariam Jamal-Hanjani, Selvaraju Veeriah, Ayse Akarca, Tom Lund, et al. “Geospatial Immune Variability Illuminates Differential Evolution of Lung Adenocarcinoma.” Nature Medicine 26, no. 7 (July 2020): 1054–62. 24 Jun 2020 Shan Raza
Lu, Ming Y., Drew F. K. Williamson, Tiffany Y. Chen, Richard J. Chen, Matteo Barbieri, and Faisal Mahmood. “Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images.” ArXiv:2004.09666 [Cs, Eess, q-Bio], May 21, 2020. 27 May 2020 Managed by Nasir.