Optimized Global Perturbation Attacks For Brain Tumour ROI Extraction From Binary Classification Models. (arXiv:2211.04926v1 [eess.IV])

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 …

arxiv attacks binary brain classification extraction global roi