A Unified Self-Supervised Deep Learning Framework for Cross-Modality Medical Image Analysis and Disease Prediction

Abstract

Deep learning has become a transformative technology in medical image analysis, significantly enhancing diagnostic accuracy and disease prediction across various clinical applications. However, the performance of supervised deep neural networks largely depends on the availability of high-quality annotated data, which is expensive and time-consuming to collect in the medical field. This paper presents a novel self-supervised deep neural network framework designed to learn efficient and transferable feature representations from unlabeled medical images. The proposed approach leverages contrastive learning and cross-modality reconstruction to extract domain-invariant features that enhance downstream classification and segmentation tasks. By integrating self-supervised pretext tasks with fine-tuning on limited labeled datasets, the model achieves robust generalization and improved diagnostic reliability across modalities such as MRI, CT, and X-ray. Experimental evaluations demonstrate that the proposed method outperforms conventional supervised baselines and recent semi-supervised learning approaches in terms of accuracy, F1-score, and area under the ROC curve. Additionally, visualization analyses reveal that self-supervised representations capture anatomical and pathological structures more effectively, supporting their interpretability in medical decision-making.