We released the Semi_Supervised_Learning library, containing the implementation of semi-supervised and semi-weakly supervised Teacher/Student learning approaches. The library is also described in the paper “Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: an experiment on prostate histopathology image classification“, published on Medical Image Analysis.
