activity
20172022
most citedSlideGraph+: Whole Slide Image Level Graphs to Predict HER2Status in Breast Cancer

15 citations · 37 across the 13 of their papers we have counts for

collaborators

19 papers

q-bio.QM2022

Insights into performance evaluation of com-pound-protein interaction prediction methods

Adiba Yaseen, Imran Amin, Naeem Akhter +2

Motivation: Machine learning based prediction of compound-protein interactions (CPIs) is important for drug design, screening and repurposing studies and can improve the efficiency…

cs.LG20221 cited

REET: Robustness Evaluation and Enhancement Toolbox for Computational Pathology

Alex Foote, Amina Asif, Nasir Rajpoot +1

Motivation: Digitization of pathology laboratories through digital slide scanners and advances in deep learning approaches for objective histological assessment have resulted in ra…

cs.CV202115 cited

SlideGraph+: Whole Slide Image Level Graphs to Predict HER2Status in Breast Cancer

Wenqi Lu, Michael Toss, Emad Rakha +2

Human epidermal growth factor receptor 2 (HER2) is an important prognostic and predictive factor which is overexpressed in 15-20% of breast cancer (BCa). The determination of its s…

cs.CV20211 cited

Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge

Mostafa Jahanifar, Adam Shephard, Neda Zamani Tajeddin +5

The detection of mitotic figures from different scanners/sites remains an important topic of research, owing to its potential in assisting clinicians with tumour grading. The MItos…

eess.IV20211 cited

All You Need is Color: Image based Spatial Gene Expression Prediction using Neural Stain Learning

Muhammad Dawood, Kim Branson, Nasir M. Rajpoot +1

"Is it possible to predict expression levels of different genes at a given spatial location in the routine histology image of a tumor section by modeling its stain absorption chara…

q-bio.QM2021

ALBRT: Cellular Composition Prediction in Routine Histology Images

Muhammad Dawood, Kim Branson, Nasir M. Rajpoot +1

Cellular composition prediction, i.e., predicting the presence and counts of different types of cells in the tumor microenvironment from a digitized image of a Hematoxylin and Eosi…