Feb. 27, 2024, 5:48 a.m. | Jay J. Yoo, Khashayar Namdar, Matthias W. Wagner, Liana Nobre, Uri Tabori, Cynthia Hawkins, Birgit B. Ertl-Wagner, Farzad Khalvati

cs.CV updates on arXiv.org arxiv.org

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

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