Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty

Weakly supervised learning (WSL) has recently triggered substantial interest as it mitigates the lack of pixel-wise annotations, while enabling interpretable models. Given global image labels, WSL methods yield pixel-level predictions (segmentations). Despite their recent success, mostly with natural images, such methods could be seriously challenged when the foreground and background regions have similar visual cues, yielding high false-positive rates in segmentations, as is the case of challenging histology images. WSL training is commonly driven by standard classification losses, which implicitly maximize model confidence and find the discriminative regions linked to classification decisions. Therefore, they lack mechanisms for modeling explicitly non-discriminative regions and reducing false-positive rates. We propose new regularization terms, which enable the model to seek both non-discriminative and discriminative regions, while discouraging unbalanced segmentations. We introduce high uncertainty as a criterion to localize non-discriminative regions that do not affect classifier decision, and describe it with original Kullback-Leibler (KL) divergence losses evaluating the deviation of posterior predictions from the uniform distribution. Our KL terms encourage high uncertainty of the model when the latter takes the latent non-discriminative regions as input. Our loss integrates: (i) a cross-entropy seeking a foreground, where model confidence about class prediction is high; (ii) a KL regularizer seeking a background, where model uncertainty is high; and (iii) log-barrier terms discouraging unbalanced segmentations. Comprehensive experiments and ablation studies over the public GlaS colon cancer data show substantial improvements over state-of-the-art WSL methods, and confirm the effect of our new regularizers. Our code is publicly available.