Aug 7, 2023 · In this paper, we redesign the OOD detection problem according to the specifics of volumetric medical imaging and related downstream tasks (e.g. ...
Oct 7, 2023 · To overcome this challenge, we propose projecting the image distribution into a one-dimensional distribution of the model's performance scores.
Sep 18, 2023 · Our findings highlight the limitations of the existing OOD detection methods with 3D medical images and present a promising avenue for improving them.
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In this paper, we further investigate OOD detection effectiveness when applied to 3D medical image segmentation. We designed several OOD challenges representing ...
We propose a new two-step algorithm to localize the vertebral column in 3D CT images and then detect individual vertebrae and quantify fractures in 2D ...
7 Excerpts. Solving Sample-Level Out-of-Distribution Detection on 3D Medical Images ... Redesigning Out-of-Distribution Detection on 3D Medical Images · A.
Feb 20, 2024 · Redesigning Out-of-Distribution Detection on 3D Medical Images. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging.
We develop Latent Transformer Models for unsupervised out-of-distribution detection in 3D. This approach combines a VQ-VAE with a transformer for likelihood ...
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We propose a simple, efficient, and accurate method for detecting out-of-distribution (OOD) data for trained neural networks. Paper