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Bayesian uncertainty-weighted loss for improved generalisability on polyp segmentation task
June 17, 2024, 4:47 a.m. | Rebecca S. Stone, Pedro E. Chavarrias-Solano, Andrew J. Bulpitt, David C. Hogg, Sharib Ali
cs.CV updates on arXiv.org arxiv.org
Abstract: While several previous studies have devised methods for segmentation of polyps, most of these methods are not rigorously assessed on multi-center datasets. Variability due to appearance of polyps from one center to another, difference in endoscopic instrument grades, and acquisition quality result in methods with good performance on in-distribution test data, and poor performance on out-of-distribution or underrepresented samples. Unfair models have serious implications and pose a critical challenge to clinical applications. We adapt an …
abstract acquisition arxiv bayesian center cs.ai cs.cv datasets difference grades loss quality replace segmentation studies type uncertainty while
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