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

• URL: http://arxiv.org/abs/2211.04926v1
• Date: Wed, 9 Nov 2022 14:52:36 GMT
• Title: Optimized Global Perturbation Attacks For Brain Tumour ROI Extraction From Binary Classification Models
• Authors: Sajith Rajapaksa, Farzad Khalvati
• Abstract summary: We propose a weakly supervised approach to obtain regions of interest using binary class labels. We also propose a novel objective function to train the generator model based on a pretrained binary classification model.
• Score: 0.304585143845864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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