63 citations · 94 across the 4 of their papers we have counts for
4 papers
GRAPHITE: Graph-Based Interpretable Tissue Examination for Enhanced Explainability in Breast Cancer Histopathology
Raktim Kumar Mondol, Ewan K. A. Millar, Peter H. Graham +3
Explainable AI (XAI) in medical histopathology is essential for enhancing the interpretability and clinical trustworthiness of deep learning models in cancer diagnosis. However, th…
MM-SurvNet: Deep Learning-Based Survival Risk Stratification in Breast Cancer Through Multimodal Data Fusion
Raktim Kumar Mondol, Ewan K. A. Millar, Arcot Sowmya +1
Survival risk stratification is an important step in clinical decision making for breast cancer management. We propose a novel deep learning approach for this purpose by integratin…
BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion
Raktim Kumar Mondol, Ewan K. A. Millar, Arcot Sowmya +1
Breast cancer is a significant health concern affecting millions of women worldwide. Accurate survival risk stratification plays a crucial role in guiding personalised treatment de…
hist2RNA: An efficient deep learning architecture to predict gene expression from breast cancer histopathology images
Raktim Kumar Mondol, Ewan K. A. Millar, Peter H Graham +3
Gene expression can be used to subtype breast cancer with improved prediction of risk of recurrence and treatment responsiveness over that obtained using routine immunohistochemist…