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Learning De-biased prototypes for Few-shot Medical Image Segmentation

Lookup NU author(s): Dr Shidong WangORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2024 Elsevier B.V.Prototypical networks have emerged as the dominant method for Few-shot Medical image Segmentation (FSMIS). Despite their success, the commonly used Masked Average Pooling (MAP) approach in prototypical networks computes the mean of the masks, resulting in imprecise and inadequate prototypes that fail to capture the subtle nuances and variations in the data. To address this issue, we propose a simple yet effective module called De-biasing Masked Average Pooling (DMAP) to generate more accurate prototypes from filtered foreground support features. Specifically, our approach introduces a Learnable Threshold Generation (LTG) module that adaptively learns a threshold based on the extracted features from both support and query images, and then choose partial foreground pixels that have larger similarity than the threshold to generate prototypes. Our proposed method is evaluated on three popular medical image segmentation datasets, and the results demonstrate the superiority of our approach over some state-of-the-art methods. Code is available at https://github.com/YazhouZhu19/DMAP.


Publication metadata

Author(s): Zhu Y, Cheng Z, Wang S, Zhang H

Publication type: Article

Publication status: Published

Journal: Pattern Recognition Letters

Year: 2024

Volume: 183

Pages: 71-77

Print publication date: 01/07/2024

Online publication date: 09/05/2024

Acceptance date: 05/05/2024

Date deposited: 22/07/2024

ISSN (print): 0167-8655

ISSN (electronic): 1872-7344

Publisher: Elsevier B.V.

URL: https://doi.org/10.1016/j.patrec.2024.05.003

DOI: 10.1016/j.patrec.2024.05.003

ePrints DOI: 10.57711/z2qg-gk21

Data Access Statement: The employed datasets are publicly available.


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Funding

Funder referenceFunder name
‘‘111’’ Program, China
National Natural Science Foundation of China
Research and Development Plan of Jiangsu Province, China

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