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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Dataset Distillation in Medical Imaging: A Feasibility Study.

Muyang Li¹, Can Cui¹, Quan Liu¹

¹Vanderbilt University, Nashville TN 37235, USA.

Proceedings of Spie--The International Society for Optical Engineering

|October 13, 2025

Summary

This summary is machine-generated.

Data distillation offers an efficient solution for medical image analysis data sharing. This method significantly reduces dataset size while maintaining comparable model performance, enabling secure collaborative research.

Keywords:

Dataset Distillation Medical Data Sharing Pattern Recognition

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Area of Science:

Medical Image Analysis
Computer Science
Data Science

Background:

Data sharing in medical imaging is crucial for model training but faces efficiency challenges.
Current methods often require transferring entire datasets, limiting collaboration.
Data distillation, a computer science technique, shows potential for efficient data sharing.

Purpose of the Study:

To investigate the applicability and effectiveness of data distillation methods in medical imaging.
To assess the impact of data distillation across diverse medical datasets.
To identify predictors for successful data distillation performance in this domain.

Main Methods:

Conducted extensive experiments using various leading data distillation techniques.
Evaluated methods across multiple medical imaging datasets with varying degrees of data variation.
Assessed model performance and identified indicators for distillation success.

Main Results:

Data distillation significantly reduces medical dataset size while preserving model performance.
Comparable results were achieved compared to using the full dataset.
A small, representative image sample can reliably indicate successful distillation.

Conclusions:

Data distillation is a viable and effective method for efficient and secure medical data sharing.
This approach can facilitate enhanced collaborative research and clinical applications.
The findings suggest potential for optimized medical data management and analysis.