ACE: Anatomically Consistent Embeddings in Composition and Decomposition

• URL: http://arxiv.org/abs/2501.10131v1
• Date: Fri, 17 Jan 2025 11:39:47 GMT
• Title: ACE: Anatomically Consistent Embeddings in Composition and Decomposition
• Authors: Ziyu Zhou, Haozhe Luo, Mohammad Reza Hosseinzadeh Taher, Jiaxuan Pang, Xiaowei Ding, Michael Gotway, Jianming Liang,
• Abstract summary: This paper introduces a novel self-supervised learning (SSL) approach called ACE to learn anatomically consistent embedding via composition and decomposition. Experimental results across 6 datasets 2 backbones, evaluated in few-shot learning, fine-tuning, and property analysis, show ACE's superior robustness, transferability, and clinical potential.
• Score: 5.939793479232325
• License:
• Abstract: Medical images acquired from standardized protocols show consistent macroscopic or microscopic anatomical structures, and these structures consist of composable/decomposable organs and tissues, but existing self-supervised learning (SSL) methods do not appreciate such composable/decomposable structure attributes inherent to medical images. To overcome this limitation, this paper introduces a novel SSL approach called ACE to learn anatomically consistent embedding via composition and decomposition with two key branches: (1) global consistency, capturing discriminative macro-structures via extracting global features; (2) local consistency, learning fine-grained anatomical details from composable/decomposable patch features via corresponding matrix matching. Experimental results across 6 datasets 2 backbones, evaluated in few-shot learning, fine-tuning, and property analysis, show ACE's superior robustness, transferability, and clinical potential. The innovations of our ACE lie in grid-wise image cropping, leveraging the intrinsic properties of compositionality and decompositionality of medical images, bridging the semantic gap from high-level pathologies to low-level tissue anomalies, and providing a new SSL method for medical imaging.

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