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Mitigating False Predictions In Unreasonable Body Regions
April 25, 2024, 7:45 p.m. | Constantin Ulrich, Catherine Knobloch, Julius C. Holzschuh, Tassilo Wald, Maximilian R. Rokuss, Maximilian Zenk, Maximilian Fischer, Michael Baumgartn
cs.CV updates on arXiv.org arxiv.org
Abstract: Despite considerable strides in developing deep learning models for 3D medical image segmentation, the challenge of effectively generalizing across diverse image distributions persists. While domain generalization is acknowledged as vital for robust application in clinical settings, the challenges stemming from training with a limited Field of View (FOV) remain unaddressed. This limitation leads to false predictions when applied to body regions beyond the FOV of the training data. In response to this problem, we propose …
abstract application arxiv challenge challenges clinical cs.cv deep learning diverse domain eess.iv false image medical predictions robust segmentation stemming training type view vital while
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