Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866908353259634688 |
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| author | Jin, Cheng Guo, Zhengrui Lin, Yi Luo, Luyang Chen, Hao |
| author_facet | Jin, Cheng Guo, Zhengrui Lin, Yi Luo, Luyang Chen, Hao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_12484 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions Jin, Cheng Guo, Zhengrui Lin, Yi Luo, Luyang Chen, Hao Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2303.12484 |