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Optimized Global Perturbation Attacks For Brain Tumour ROI Extraction From Binary Classification Models
Sajith Rajapaksa · Farzad Khalvati

Deep learning techniques have greatly benefited computer-aided diagnostic systems. However, unlike in 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 obtaining 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.

Author Information

Sajith Rajapaksa (University of Toronto / SickKids)
Farzad Khalvati (University of Toronto)

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