Learning shape distributions from large databases of healthy organs: applications to zero-shot and few-shot abnormal pancreas detection

• URL: http://arxiv.org/abs/2210.12095v1
• Date: Fri, 21 Oct 2022 16:39:59 GMT
• Title: Learning shape distributions from large databases of healthy organs: applications to zero-shot and few-shot abnormal pancreas detection
• Authors: Rebeca V\'etil, Cl\'ement Abi Nader, Alexandre B\^one, Marie-Pierre Vullierme, Marc-Michel Rohe\'e, Pietro Gori, Isabelle Bloch
• Abstract summary: We propose a scalable and data-driven approach to learn shape distributions from large databases of healthy organs. The resulting latent shape representations are leveraged to derive zeroshot and few-shot methods for abnormal shape detection.
• Score: 56.4729555813972
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a scalable and data-driven approach to learn shape distributions from large databases of healthy organs. To do so, volumetric segmentation masks are embedded into a common probabilistic shape space that is learned with a variational auto-encoding network. The resulting latent shape representations are leveraged to derive zeroshot and few-shot methods for abnormal shape detection. The proposed distribution learning approach is illustrated on a large database of 1200 healthy pancreas shapes. Downstream qualitative and quantitative experiments are conducted on a separate test set of 224 pancreas from patients with mixed conditions. The abnormal pancreas detection AUC reached up to 65.41% in the zero-shot configuration, and 78.97% in the few-shot configuration with as few as 15 abnormal examples, outperforming a baseline approach based on the sole volume.

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