AFExNet: An Adversarial Autoencoder for Differentiating Breast Cancer Sub-Types and Extracting Biologically Relevant Genes. - Raktim Kumar Mondol; Nhan Duy Truong; Mohammad Reza; Samuel J. Ippolito; Esmaeil Ebrahimie; Omid Kavehei (IEEE ACM Trans. Comput. Biol. Bioinform., 2022)

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; Lois Browne; Arcot Sowmya; Erik Meijering (CoRR, 2023)

MM-Survnet: Deep Learning-Based Survival Risk Stratification in Breast Cancer Through Multimodal Data Fusion. - Raktim Kumar Mondol; Ewan K. A. Millar; Arcot Sowmya; Erik Meijering (ISBI, 2024)

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; Erik Meijering (IEEE J. Biomed. Health Informatics, 2024)

GRAPHITE: Graph-Based Interpretable Tissue Examination for Enhanced Explainability in Breast Cancer Histopathology. - Raktim Kumar Mondol; Ewan K. A. Millar; Peter H. Graham; Lois Browne; Arcot Sowmya; Erik Meijering (CoRR, 2025)