Fig. 1

The main components of our approach: Datasets for PCa grading with strong and weak labels ("Datasets" section, CNN model training with three strategies "Datasets" section and third, the tests performed in two scenarios of PCa grading: tissue microarrays of prostate tissue and prostactectomy WSIs, "Results" section. The patches from the strongly labeled TMAs are used to train CNN models with an increasing number of annotations, evaluating the performance depending on the number of strong labels used for training. The ImageNet pre-trained models are either trained using only the weak WSI-level label or fine-tuned with WSI patches and weak labels, combining different sources of supervision. The models are tested in the two scenarios of PCa grading: tissue-microarrays, and prostactectomies. Arrows of the same color indicate the data or model input from the previous step