27 citations · 31 across the 5 of their papers we have counts for
5 papers
Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images
Md Mamunur Rahaman, Ewan K. A. Millar, Erik Meijering
Zero-shot learning holds tremendous potential for histopathology image analysis by enabling models to generalize to unseen classes without extensive labeled data. Recent advancemen…
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…
Semi-supervised variational autoencoder for cell feature extraction in multiplexed immunofluorescence images
Piumi Sandarenu, Julia Chen, Iveta Slapetova +6
Advancements in digital imaging technologies have sparked increased interest in using multiplexed immunofluorescence (mIF) images to visualise and identify the interactions between…
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…