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UOD: Universal One-Shot Detection of Anatomical Landmarks

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (MICCAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14220))

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Abstract

One-shot medical landmark detection gains much attention and achieves great success for its label-efficient training process. However, existing one-shot learning methods are highly specialized in a single domain and suffer domain preference heavily in the situation of multi-domain unlabeled data. Moreover, one-shot learning is not robust that it faces performance drop when annotating a sub-optimal image. To tackle these issues, we resort to developing a domain-adaptive one-shot landmark detection framework for handling multi-domain medical images, named Universal One-shot Detection (UOD). UOD consists of two stages and two corresponding universal models which are designed as combinations of domain-specific modules and domain-shared modules. In the first stage, a domain-adaptive convolution model is self-supervised learned to generate pseudo landmark labels. In the second stage, we design a domain-adaptive transformer to eliminate domain preference and build the global context for multi-domain data. Even though only one annotated sample from each domain is available for training, the domain-shared modules help UOD aggregate all one-shot samples to detect more robust and accurate landmarks. We investigated both qualitatively and quantitatively the proposed UOD on three widely-used public X-ray datasets in different anatomical domains (i.e., head, hand, chest) and obtained state-of-the-art performances in each domain. The code is at https://github.com/heqin-zhu/UOD_universal_oneshot_detection.

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Acknowledgment

Supported by Natural Science Foundation of China under Grant 62271465 and Open Fund Project of Guangdong Academy of Medical Sciences, China (No. YKY-KF202206).

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Correspondence to S. Kevin Zhou .

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Zhu, H., Quan, Q., Yao, Q., Liu, Z., Zhou, S.K. (2023). UOD: Universal One-Shot Detection of Anatomical Landmarks. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14220. Springer, Cham. https://doi.org/10.1007/978-3-031-43907-0_3

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  • DOI: https://doi.org/10.1007/978-3-031-43907-0_3

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