Interpretable Prostate Cancer Detection using a Small Cohort of MRI Images

TL;DR

This study presents an interpretable AI framework for prostate cancer detection using a small MRI dataset, achieving high accuracy with transfer learning and handcrafted features, and demonstrating potential to assist radiologists.

Contribution

It introduces a small-cohort prostate cancer detection method that relies on transfer learning and handcrafted features, reducing data and computational requirements.

Findings

ResNet18 achieved 90.9% accuracy and 95.2% sensitivity.

HOG+SVM achieved comparable accuracy with AUC 0.917.

AI model outperformed radiologists in sensitivity (95.2% vs. 67.5%).

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.