Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions

• URL: http://arxiv.org/abs/2303.12484v5
• Date: Thu, 08 May 2025 13:51:45 GMT
• Title: Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions
• Authors: Cheng Jin, Zhengrui Guo, Yi Lin, Luyang Luo, Hao Chen,
• Abstract summary: Label-efficient deep learning methods have emerged to improve model performance under limited supervision.<n>These methods are categorized into four labeling paradigms: no label, insufficient label, inexact label, and label refinement.<n>We identify current challenges and future directions to facilitate the translation of label-efficient learning from research promise to everyday clinical care.
• Score: 9.789815598574737
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep learning has significantly advanced medical imaging analysis (MIA), achieving state-of-the-art performance across diverse clinical tasks. However, its success largely depends on large-scale, high-quality labeled datasets, which are costly and time-consuming to obtain due to the need for expert annotation. To mitigate this limitation, label-efficient deep learning methods have emerged to improve model performance under limited supervision by leveraging labeled, unlabeled, and weakly labeled data. In this survey, we systematically review over 350 peer-reviewed studies and present a comprehensive taxonomy of label-efficient learning methods in MIA. These methods are categorized into four labeling paradigms: no label, insufficient label, inexact label, and label refinement. For each category, we analyze representative techniques across imaging modalities and clinical applications, highlighting shared methodological principles and task-specific adaptations. We also examine the growing role of health foundation models (HFMs) in enabling label-efficient learning through large-scale pre-training and transfer learning, enhancing the use of limited annotations in downstream tasks. Finally, we identify current challenges and future directions to facilitate the translation of label-efficient learning from research promise to everyday clinical care.

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