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

Sajith Rajapaksa; Farzad Khalvati

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arXiv · 2211.04926

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

Sajith Rajapaksa·Farzad Khalvati

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Abstract

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.

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Sajith Rajapaksa, Farzad Khalvati. 2022-11-09. Optimized Global Perturbation Attacks For Brain Tumour ROI Extraction From Binary Classification Models. https://arxiv.org/abs/2211.04926

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