FSS-ULivR: a clinically-inspired few-shot segmentation framework for liver imaging using unified representations and attention mechanisms

FSS-ULivR: a clinically-inspired few-shot segmentation framework for liver imaging using unified representations and attention mechanisms

Abstract Precise liver segmentation is critical for accurate diagnosis and effective treatment planning, serving as a foundation for medical image analysis. However, existing methods struggle with limited labeled data, poor generalizability, and insufficient integration of anatomical and clinical fe...

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Bibliographic Details
Main Authors: Ripon Kumar Debnath, Md. Abdur Rahman, Sami Azam, Yan Zhang, Mirjam Jonkman
Format: Article
Language:English
Published: Springer 2025-07-01
Series:Journal of Cancer Research and Clinical Oncology
Subjects:
Few-shot learning
Medical image segmentation
Prototype-based learning
Transformer networks
Cross-dataset evaluation
Online Access:https://doi.org/10.1007/s00432-025-06256-0
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https://doi.org/10.1007/s00432-025-06256-0

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