  Pointwise localization allows more precise localization and accurate
interpretability, compared to bounding box, in applications where objects are
highly unstructured such as in medical domain. In this work, we focus on weakly
supervised localization (WSL) where a model is trained to classify an image and
localize regions of interest at pixel-level using only global image annotation.
Typical convolutional attentions maps are prune to high false positive regions.
To alleviate this issue, we propose a new deep learning method for WSL,
composed of a localizer and a classifier, where the localizer is constrained to
determine relevant and irrelevant regions using conditional entropy (CE) with
the aim to reduce false positive regions. Experimental results on a public
medical dataset and two natural datasets, using Dice index, show that, compared
to state of the art WSL methods, our proposal can provide significant
improvements in terms of image-level classification and pixel-level
localization (low false positive) with robustness to overfitting. A public
reproducible PyTorch implementation is provided in:
https://github.com/sbelharbi/wsol-min-max-entropy-interpretability .
