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Multi-challenge transfer getting to know (MTTL) is a technique utilized in machine studying to improve the performance of one or extra models employing leveraging understanding from associated tasks. This article examines the benefit of MTTLC for medical photo segmentation. They segmented clinical photos with a deep learning model to compare the two, using a multi-mission method rather than only one undertaking. The authors develop a framework to teach two models, one consistent with each task, and explore the usage of MTTLC to refine the second version. The results show that while the single project network can attain proper performance, the MTTLC technique can significantly enhance segmentation accuracy. The results suggest that MTTLC could be a beneficial tool in scientific imaging, and extra research is needed to explore its ability and benefits.

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Investigating the Benefits of Multi-Task Transfer Learning for Medical Image Segmentation

  • 会议论文

  • 发表日期：

    2025年

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  • Priyanka Chandani,
  • Feon Jaison,
  • Megha Pandeya,
  • Neeraj Sharma

摘要

Multi-challenge transfer getting to know (MTTL) is a technique utilized in machine studying to improve the performance of one or extra models employing leveraging understanding from associated tasks. This article examines the benefit of MTTLC for medical photo segmentation. They segmented clinical photos with a deep learning model to compare the two, using a multi-mission method rather than only one undertaking. The authors develop a framework to teach two models, one consistent with each task, and explore the usage of MTTLC to refine the second version. The results show that while the single project network can attain proper performance, the MTTLC technique can significantly enhance segmentation accuracy. The results suggest that MTTLC could be a beneficial tool in scientific imaging, and extra research is needed to explore its ability and benefits.