UMS-Rep: Unified modality-specific representation for efficient medical image analysis.

Area of Science:

• Medical image analysis
• Deep learning
• Artificial intelligence in healthcare

Background:

• Traditional medical image analysis uses separate deep learning models for each task, leading to inefficiencies.
• This approach demands significant computational resources and large labeled datasets.
• Task-specific models hinder knowledge transfer and optimal performance.

Purpose of the Study:

• To propose an efficient multi-task learning framework for medical image analysis.
• To enable simultaneous fine-tuning of diverse tasks via knowledge transfer.
• To investigate the impact of fine-tuning strategies on task performance.

Main Methods:

• Developed a unified modality-specific feature representation (UMS-Rep) for multi-task training.
• Implemented simultaneous fine-tuning of tasks like image denoising, segmentation, and classification.
• Experimented on chest X-ray and Doppler echocardiography imaging modalities.

Main Results:

• The multi-task approach significantly reduced computational time (up to 86%) and resource demand.
• Achieved improved accuracy (up to 9%) in target medical image analysis tasks.
• Demonstrated that fine-tuning strategies critically influence overall performance.

Conclusions:

• The proposed unified multi-task learning approach enhances efficiency and performance in medical image analysis.
• Knowledge transfer through UMS-Rep is effective across different imaging modalities and tasks.
• Optimizing fine-tuning strategies is crucial for maximizing benefits in medical AI applications.