Web: http://arxiv.org/abs/2208.02870

Sept. 16, 2022, 1:15 a.m. | Cheng Ouyang, Shuo Wang, Chen Chen, Zeju Li, Wenjia Bai, Bernhard Kainz, Daniel Rueckert

cs.CV updates on arXiv.org arxiv.org

Probability calibration for deep models is highly desirable in
safety-critical applications such as medical imaging. It makes output
probabilities of deep networks interpretable, by aligning prediction
probability with the actual accuracy in test data. In image segmentation,
well-calibrated probabilities allow radiologists to identify regions where
model-predicted segmentations are unreliable. These unreliable predictions
often occur to out-of-domain (OOD) images that are caused by imaging artifacts
or unseen imaging protocols. Unfortunately, most previous calibration methods
for image segmentation perform sub-optimally on OOD …

arxiv post probability segmentation

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