  CNN visualization and interpretation methods, like class-activation maps
(CAMs), are typically used to highlight the image regions linked to class
predictions. These models allow to simultaneously classify images and extract
class-dependent saliency maps, without the need for costly pixel-level
annotations. However, they typically yield segmentations with high
false-positive rates and, therefore, coarse visualisations, more so when
processing challenging images, as encountered in histology. To mitigate this
issue, we propose an active learning (AL) framework, which progressively
integrates pixel-level annotations during training. Given training data with
global image-level labels, our deep weakly-supervised learning model jointly
performs supervised image-level classification and active learning for
segmentation, integrating pixel annotations by an oracle. Unlike standard AL
methods that focus on sample selection, we also leverage large numbers of
unlabeled images via pseudo-segmentations (i.e., self-learning at the pixel
level), and integrate them with the oracle-annotated samples during training.
We report extensive experiments over two challenging benchmarks --
high-resolution medical images (histology GlaS data for colon cancer) and
natural images (CUB-200-2011 for bird species). Our results indicate that, by
simply using random sample selection, the proposed approach can significantly
outperform state-of the-art CAMs and AL methods, with an identical
oracle-supervision budget. Our code is publicly available.
