Fig. 3: Overall architecture of BioNet.

BioNet consists of two networks: BioNet_Neu to predict Neu using MRI; BioNet_ProInf to simultaneously predict Pro and Inf using MRI. a BioNet_Neu is a feedforward neural network pre-trained using a large number of unlabeled samples with noisy Neu labels informed by biological knowledge, and fine-tuned using biopsy samples with data augmentation. It also corporates Monte Carlo dropout to enable uncertainty quantification for the predictions. The role of BioNet_Neu is to stratify unlabeled samples with high predictive certainty, which were then incorporated into the training of BioNet_ProInf. b BioNet_ProInf is a multitask semi-supervised learning model with a custom loss function. The architecture consists of a shared block and task-specific blocks. The loss function combines a prediction loss and a knowledge attention loss that penalizes violation of the knowledge-based relationships on unlabeled samples.