  Deep learning techniques have greatly benefited computer-aided diagnostic
systems. However, unlike other fields, in medical imaging, acquiring large
fine-grained annotated datasets such as 3D tumour segmentation is challenging
due to the high cost of manual annotation and privacy regulations. This has
given interest to weakly-supervise methods to utilize the weakly labelled data
for tumour segmentation. In this work, we propose a weakly supervised approach
to obtain regions of interest using binary class labels. Furthermore, we
propose a novel objective function to train the generator model based on a
pretrained binary classification model. Finally, we apply our method to the
brain tumour segmentation problem in MRI.
