Improving Automated Prostate Cancer Detection and Classification Accuracy with Multi-scale Cancer Information

Abstract

Automated detection of prostate cancer via multi-parametric Magnetic Resonance Imaging (mp-MRI) could help radiologists in the detection and localization of cancer. Several existing deep learning-based prostate cancer detection methods have high cancer detection sensitivity but suffer from high rates of false positives and misclassification between indolent (Gleason Pattern = 3) and aggressive (Gleason Pattern $\ge$ 4) cancer. In this work, we propose a multi-scale Decision Prediction Module (DPM), a novel lightweight false-positive reduction module that can be added to cancer detection models to reduce false positives, while maintaining high sensitivity. The module guides pixel-level predictions with local context information inferred from multi-resolution coarse labels, which are derived from ground truth pixel-level labels with patch-wise calculation. The coarse label resolution varies from a quarter size and 16 times smaller, to a single label for the whole slice, indicating that the slice is normal, indolent, or aggressive. We also propose a novel multi-scale decision loss that supervises cancer prediction at each resolution. Evaluated on an internal test set of 56 studies, our proposed model, DecNet, which adds the DPM and multi-scale loss to the baseline model SPCNet, significantly increases precision from 0.49 to 0.63 ($p \le 0.005$ in paired t-test) while keeping the same level of sensitivity (0.90) for clinically significant cancer predictions. Our model also significantly outperforms U-Net in sensitivity and Dice coefficient ($p \le 0.05$ and $p \le 0.005$, respectively). As shown in the appendix, a similar trend was found when validating with an external dataset containing multi-vendor MRI exams. An ablation study on different label resolutions of the DPM shows that decision loss at all three scales achieves the best performance.