Symmetry Awareness Encoded Deep Learning Framework for Brain Imaging Analysis

TL;DR

This paper introduces a symmetry-aware deep learning framework that leverages the brain's inherent anatomical symmetry to improve brain disease detection and segmentation, demonstrating superior performance on MRI and CT datasets.

Contribution

The study proposes a novel Symmetry-Aware Cross-Attention module and a symmetry detection proxy task for pretraining, enhancing analysis accuracy in brain imaging tasks.

Findings

Outperforms state-of-the-art methods in classification and segmentation.

Effective use of symmetry features improves model robustness.

Sets new benchmarks in medical imaging analysis.

Abstract

The heterogeneity of neurological conditions, ranging from structural anomalies to functional impairments, presents a significant challenge in medical imaging analysis tasks. Moreover, the limited availability of well-annotated datasets constrains the development of robust analysis models. Against this backdrop, this study introduces a novel approach leveraging the inherent anatomical symmetrical features of the human brain to enhance the subsequent detection and segmentation analysis for brain diseases. A novel Symmetry-Aware Cross-Attention (SACA) module is proposed to encode symmetrical features of left and right hemispheres, and a proxy task to detect symmetrical features as the Symmetry-Aware Head (SAH) is proposed, which guides the pretraining of the whole network on a vast 3D brain imaging dataset comprising both healthy and diseased brain images across various MRI and CT. Through meticulous experimentation on downstream tasks, including both classification and segmentation for brain diseases, our model demonstrates superior performance over state-of-the-art methodologies, particularly highlighting the significance of symmetry-aware learning. Our findings advocate for the effectiveness of incorporating symmetry awareness into pretraining and set a new benchmark for medical imaging analysis, promising significant strides toward accurate and efficient diagnostic processes. Code is available at https://github.com/bitMyron/sa-swin.