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We use our analysis and experiments to summarize the benefits of using segmentation-for-classification, including: improved sample efficiency, enabling improved ...
We reexamine the choice of training classification vs. segmentation models. First, we use an information theoretic approach to analyze why segmentation vs. ...
May 30, 2024 · In light of this recent work, we reexamine the choice of training classification vs. segmentation models. First, we use an information theoretic ...
A case for reframing automated medical image classification as segmentation · Sarah Hooper, Mayee F. Chen, +3 authors. Christopher Ré · Published in Neural ...
A case for reframing automated medical image classification as segmentation. NeurIPS, 2023. 2. M. Varma, J.B. Delbrouck, S. M. Hooper, A. Chaudhari, C ...
A case for reframing automated medical image classification as segmentation. S Hooper, M Chen, K Saab, K Bhatia, C Langlotz, C Ré. Advances in Neural ...
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This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images.
Missing: reframing | Show results with:reframing
In this work, we provide a comprehensive overview of recent endeavors aimed at extending the efficacy of SAM to medical image segmentation tasks.
Missing: reframing | Show results with:reframing
May 15, 2021 · In this blog, we are doing to discuss a case study called 'SIIM-ACR Pneumothorax Segmentation' that includes detection of disease from chest X-rays.