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In the realm of natural image analysis, the availability of large-scale, high-quality annotated datasets has significantly propelled the advancement of deep learning models. However, different from the nature image, acquisition of high-quality annotated medical images is a more challenging and resource-intensive task, primarily due to the necessity of expert knowledge for accurate labeling. Consequently, the scarcity of annotated data in medical imaging poses a substantial barrier to the development and deployment of robust deep learning models.

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Semi-Supervised Learning for Medical Image Analysis

  • 章节

  • 发表日期：

    2026年

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  • Yen-Wei Chen,
  • Lanfen Lin,
  • Rahul Kumar Jain

摘要

In the realm of natural image analysis, the availability of large-scale, high-quality annotated datasets has significantly propelled the advancement of deep learning models. However, different from the nature image, acquisition of high-quality annotated medical images is a more challenging and resource-intensive task, primarily due to the necessity of expert knowledge for accurate labeling. Consequently, the scarcity of annotated data in medical imaging poses a substantial barrier to the development and deployment of robust deep learning models.