Prostate Gleason score prediction via MRI using capsule network

Magnetic Resonance imaging (MRI) is a non-invasive modality for diagnosing prostate carcinoma (PCa) and deep learning has gained increasing interest in MR images. We propose a novel 3D Capsule Network to perform low grade vs high grade PCa classification. The proposed network utilizes Efficient CapsNet as backbone and consists of three main components, 3D convolutional blocks, depth-wise separable 3D convolution, and self-attention routing. The network employs convolutional blocks to extract high level features, which will form primary capsules via depth-wise separable convolution operations. A self-attention mechanism is used to route primary capsules to higher level capsules and finally a PCa grade is assigned. The proposed 3D Capsule Network was trained and tested using a public dataset that involves 529 patients diagnosed with PCa. A baseline 3D CNN method was also experimented for comparison. Our Capsule Network achieved 85% accuracy and 0.87 AUC, while the baseline CNN achieved 80% accuracy and 0.84 AUC. The superior performance of Capsule Network demonstrates its feasibility for PCa grade classification from prostate MRI and shows its potential in assisting clinical decision-making.