# MedCSS: a causal self-supervised approach for hierarchical feature consistency in 3D medical imaging

## TL;DR

MedCSS is a new method for 3D medical imaging that improves model robustness by using causal self-supervised learning to align hierarchical features.

## Contribution

Introduces MedCSS, a causal self-supervised framework for 3D medical imaging with hierarchical feature consistency and causal regularization.

## Key findings

- MedCSS improves feature stability and generalization in 3D medical imaging.
- The method enhances boundary sensitivity and morphological coherence in diverse medical structures.
- Experiments show improved performance on the MedMNIST3D benchmark.

## Abstract

Medical image analysis plays a crucial role in linking perceptual mechanisms with clinical diagnosis, yet conventional deep learning models often rely on statistical correlations rather than modeling the underlying generative structure, leading to limited robustness in small-sample and cross-domain scenarios. To address this issue, we propose a hierarchical feature consistency framework named “MedCSS” that integrates causal self-supervised learning. Built upon a 3D ResNet backbone, the method aligns intermediate and high-level features through distributional consistency while introducing a coding rate–based causal regularization to suppress non-causal redundancy. Experiments on the MedMNIST3D benchmark demonstrate enhanced feature stability, boundary sensitivity, and generalization across diverse medical structures. Visualization analyses further reveal improved morphological coherence and causal interpretability. This study highlights the potential of causal self-supervision for structurally robust and semantically consistent representation learning in three-dimensional medical imaging.