Advancing Healthcare: Innovative ML Approaches for Improved Medical Imaging in Data-Constrained Environments

TL;DR

This paper introduces the CAT-U-Net framework, a novel deep learning approach that improves medical image reconstruction and diagnosis in data-scarce environments while preserving patient privacy.

Contribution

The paper presents the CAT-U-Net model, which enhances feature extraction from limited medical data using an added concatenation layer, without requiring large datasets.

Findings

Achieved nearly 98% reconstruction accuracy.

Attained a Dice coefficient close to 0.946.

Validated across diverse medical datasets.

Abstract

Healthcare industries face challenges when experiencing rare diseases due to limited samples. Artificial Intelligence (AI) communities overcome this situation to create synthetic data which is an ethical and privacy issue in the medical domain. This research introduces the CAT-U-Net framework as a new approach to overcome these limitations, which enhances feature extraction from medical images without the need for large datasets. The proposed framework adds an extra concatenation layer with downsampling parts, thereby improving its ability to learn from limited data while maintaining patient privacy. To validate, the proposed framework's robustness, different medical conditioning datasets were utilized including COVID-19, brain tumors, and wrist fractures. The framework achieved nearly 98% reconstruction accuracy, with a Dice coefficient close to 0.946. The proposed CAT-U-Net has the potential to make a big difference in medical image diagnostics in settings with limited data.