Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images

arXiv:2211.05269v2 Announce Type: replace-cross
Abstract: Segmentation of regions of interest (ROIs) for identifying abnormalities is a leading problem in medical imaging. Using machine learning for this problem generally requires manually annotated ground-truth segmentations, demanding extensive time and resources from radiologists. This work presents a weakly supervised approach that utilizes binary image-level labels, which are much simpler to acquire, to effectively segment anomalies in 2D magnetic resonance images without ground truth annotations. We train a generative adversarial network (GAN) that converts …

abstract adversarial arxiv brain cs.cv eess.iv evaluation generative generative adversarial networks ground-truth images imaging machine machine learning medical medical imaging networks resources segmentation truth type work