Large numbers of histopathological images have been digitized into high resolution whole slide images, opening opportunities in developing computational image analysis tools to reduce pathologists' workload and potentially improve inter- and intra- observer agreement.
The coarse functional distinction between these streams is between object recognition -- the "what" of the signal -- and extracting location related information -- the "where" of the signal.
In this work, we investigate semi-supervised learning (SSL) for image classification using adversarial training.
The model achieved state-of-the-art performance for prostate cancer grading with an accuracy of 85. 11\% for classifying benign, low-grade (Gleason grade 3+3 or 3+4), and high-grade (Gleason grade 4+3 or higher) slides on an independent test set.
Recently, semi-supervised learning methods based on generative adversarial networks (GANs) have received much attention.
Ideally, attention maps predicted by captioning models should be consistent with intrinsic attentions from visual models for any given visual concept.