Optimized Global Perturbation Attacks For Brain Tumour ROI Extraction From Binary Classification Models
This addresses the problem of limited annotated data in medical imaging for researchers and clinicians, though it appears incremental as it builds on existing weakly supervised methods.
The paper tackles the challenge of brain tumor segmentation in MRI by proposing a weakly supervised approach that uses binary class labels to extract regions of interest, achieving results without requiring large fine-grained annotated datasets.
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.