Training Deep Networks for Prostate Cancer Diagnosis Using Coarse Histopathological Labels

Motivation: Accurate detection of prostate cancer using ultrasound data is a challenging yet highly relevant clinical question. A significant roadblock for training accurate models for cancer detection is the lack of histopathology labels with high resolution that correspond to the presence of cancer in the entire imaging or biopsy planes. Histopathology reports only provide a coarse, representation of cancer distribution in an image region; the distribution of cancer in itself is only approximately reported, making labels generated from these reports very noisy. Method: We propose a multi-constraint optimization method in a co-teaching framework with two deep neural networks. These networks are simultaneously and jointly trained, where each network uses data identified by its peer network as less noisy, to update itself. We propose two additional constraints based on the statistics of cancer distribution and noisy nature of labels to the conventional co-teaching framework. Results: We demonstrate the effectiveness of the proposed learning methodology using a challenging ultrasound dataset with 380 biopsy cores obtained from 89 patients during systematic prostate biopsy. Our results show that our proposed multi-constraint optimization method leads to a significant improvements in terms of area under the curve and balanced accuracy over baseline co-teaching method for detection of prostate cancer.