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Task-Coupling Elastic Learning for Physical Sign-Based Medical Image Classification.

Yingxue Xu, Guihua Wen, Pei Yang

IEEE Journal of Biomedical and Health Informatics

|August 24, 2021

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This study introduces a novel Task-Coupling Elastic Learning (TCEL) framework for disease diagnosis using physical signs. TCEL dynamically adjusts knowledge transfer between disease-location and disease-nature classification tasks, improving diagnostic accuracy.

Area of Science:

Medical imaging analysis
Machine learning in healthcare
Computer-assisted diagnosis

Background:

Physical signs are vital for diagnosing disease location and nature, presenting a sequential relationship.
Joint learning can leverage intrinsic associations between these diagnostic tasks.
Optimizing the timing of knowledge transfer in joint learning remains a challenge.

Purpose of the Study:

To propose a Task-Coupling Elastic Learning (TCEL) framework for classifying disease-location and disease-nature from physical sign images.
To dynamically adjust knowledge transfer between tasks during multi-stage training.
To address the open issue of when to transfer knowledge for optimal joint learning.

Main Methods:

Developed a Task-Coupling Elastic Learning (TCEL) framework with a dynamic sequential module (DSM).

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Implemented a multi-stage training approach, progressing from loose to tight task-coupling.

Introduced a novel loss regularization to mitigate side effects of the DSM.

Main Results:

The proposed TCEL framework demonstrated superior performance compared to baseline methods on two clinical datasets.
Experiments validated the effectiveness of the dynamic task-coupling mechanism.
The method successfully improved the classification of disease-location and disease-nature.

Conclusions:

The TCEL framework offers an effective approach for dynamically transferring knowledge in joint learning tasks.
The proposed method enhances disease diagnosis accuracy by optimizing task interactions.
This work provides a novel solution for the critical problem of timing knowledge transfer in medical image analysis.