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Deep learning techniques have greatly benefited computer-aided diagnosticsystems. However, unlike other fields, in medical imaging, acquiring largefine-grained annotated datasets such as 3D tumour segmentation is challengingdue to the high cost of manual annotation and privacy regulations. This hasgiven interest to weakly-supervise methods to utilize the weakly labelled datafor tumour segmentation. In this work, we propose a weakly supervised approachto obtain regions of interest using binary class labels. Furthermore, wepropose a novel objective function to train the generator model based on apretrained binary classification model. Finally, we apply our method to thebrain tumour segmentation problem in MRI.

Optimized Global Perturbation Attacks For Brain Tumour ROI Extraction From Binary Classification Models