activity
20172025
most citedPredicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge

331 citations · 592 across the 16 of their papers we have counts for

collaborators

16 papers

cs.CV2025

PS3: A Multimodal Transformer Integrating Pathology Reports with Histology Images and Biological Pathways for Cancer Survival Prediction

Manahil Raza, Ayesha Azam, Talha Qaiser +1

Current multimodal fusion approaches in computational oncology primarily focus on integrating multi-gigapixel histology whole slide images (WSIs) with genomic or transcriptomic dat…

eess.IV2025

A Novel Approach to Linking Histology Images with DNA Methylation

Manahil Raza, Muhammad Dawood, Talha Qaiser +1

DNA methylation is an epigenetic mechanism that regulates gene expression by adding methyl groups to DNA. Abnormal methylation patterns can disrupt gene expression and have been li…

cs.CV2024★ 1 cited

Stain-Invariant Representation for Tissue Classification in Histology Images

Manahil Raza, Saad Bashir, Talha Qaiser +1

The process of digitising histology slides involves multiple factors that can affect a whole slide image's (WSI) final appearance, including the staining protocol, scanner, and tis…

cs.CV2023

Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classification

Sajid Javed, Arif Mahmood, Talha Qaiser +2

Classification of gigapixel Whole Slide Images (WSIs) is an important prediction task in the emerging area of computational pathology. There has been a surge of research in deep le…

eess.IV2023★ 3 cited

Interpretable histopathology-based prediction of disease relevant features in Inflammatory Bowel Disease biopsies using weakly-supervised deep learning

Ricardo Mokhtari, Azam Hamidinekoo, Daniel Sutton +12

Crohn's Disease (CD) and Ulcerative Colitis (UC) are the two main Inflammatory Bowel Disease (IBD) types. We developed deep learning models to identify histological disease feature…

cs.CV2023

Consistency Regularisation in Varying Contexts and Feature Perturbations for Semi-Supervised Semantic Segmentation of Histology Images

Raja Muhammad Saad Bashir, Talha Qaiser, Shan E Ahmed Raza +1

Semantic segmentation of various tissue and nuclei types in histology images is fundamental to many downstream tasks in the area of computational pathology (CPath). In recent years…