Interpretable Prostate Cancer Detection using a Small Cohort of MRI Images
Organizations: Department of Computer Science, Boston University Metropolitan College, Boston, MA, USA · MD, Clinical Fellow, Department of Radiology, McMaster University, Ontario, Canada · Department of Radiology, School of Medicine, 5th Azar Hospital, Radiology, Golestan University of Medical Sciences, Gorgan, Iran · MD, FRCPC, Department of Medical Imaging, Niagara Health System, Assistant Clinical Professor, Department of Radiology, McMaster University · MD, Department of Radiology, School of Medicine, 5th Azar Hospital, Golestan University of Medical Sciences, Gorgan, Golestan, Iran. · MD, FRCPC, Department of Medical Imaging, Niagara Health System, Associate Clinical Professor, Department of Radiology, McMaster University · MD, FRCPC, Department of Medical Imaging, Niagara Health System, Ontario, Canada · Adjunct clinical professor, department of radiology, McMaster University, Ontario, Canada · Center of Excellence in Precision Medicine and Digital Health, Department of Physiology, Chulalongkorn University, Thailand
Abstract
Prostate cancer is a leading cause of mortality in men, yet interpretation of T2-weighted prostate MRI remains challenging due to subtle and heterogeneous lesions. We developed an interpretable framework for automatic cancer detection using a small dataset of 162 T2-weighted images (102 cancer, 60 normal), addressing data scarcity through transfer learning and augmentation. We performed a comprehensive comparison of Vision Transformers (ViT, Swin), CNNs (ResNet18), and classical methods (Logistic Regression, SVM, HOG+SVM). Transfer-learned ResNet18 achieved the best performance (90.9% accuracy, 95.2% sensitivity, AUC 0.905) with only 11M parameters, while Vision Transformers showed lower performance despite substantially higher complexity. Notably, HOG+SVM achieved comparable accuracy (AUC 0.917), highlighting the effectiveness of handcrafted features in small datasets. Unlike state-of-the-art approaches relying on biparametric MRI (T2+DWI) and large cohorts, our method achieves competitive performance using only T2-weighted images, reducing acquisition complexity and computational cost. In a reader study of 22 cases, five radiologists achieved a mean sensitivity of 67.5% (Fleiss Kappa = 0.524), compared to 95.2% for the AI model, suggesting potential for AI-assisted screening to reduce missed cancers and improve consistency. Code and data are publicly available.