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Symmetry Awareness Encoded Deep Learning Framework for Brain Imaging Analysis

Yang Ma, Dongang Wang, Peilin Liu, Lynette Masters, Michael Barnett, Weidong Cai, Chenyu Wang

TL;DR

The paper addresses data scarcity and heterogeneity in brain imaging by leveraging inherent hemispheric symmetry. It introduces Symmetry-Aware Cross-Attention (SACA) to compare a patch with its mirrored counterpart using a Swin Transformer backbone, and a Symmetry-Aware Head (SAH) that guides symmetry-focused pretraining via four proxy tasks, including an explicit symmetry loss defined by $\mathcal{L}_{Symmetry}$ and the cross-attention formula $\operatorname{SACA}(\f, \tilde{\f}) = \operatorname{Softmax}\left(\frac{Q \tilde{K}^T}{\sqrt{D}}\right) \tilde{V}$ with $Q=f W_Q$, $\tilde{K}=\tilde{f} W_K$, $\tilde{V}=\tilde{f} W_V$. Pretraining on large MRI and CT datasets yields strong zero-shot, few-shot, and downstream performance on brain disease identification and lesion segmentation, achieving state-of-the-art results on benchmarks such as BraTS2021 and CQ500. The findings demonstrate the practical value of encoding anatomical symmetry in medical imaging, offering improved diagnostic accuracy and generalization while reducing the need for extensive labeled data; code is available at the authors' GitHub repository.

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.

Symmetry Awareness Encoded Deep Learning Framework for Brain Imaging Analysis

TL;DR

The paper addresses data scarcity and heterogeneity in brain imaging by leveraging inherent hemispheric symmetry. It introduces Symmetry-Aware Cross-Attention (SACA) to compare a patch with its mirrored counterpart using a Swin Transformer backbone, and a Symmetry-Aware Head (SAH) that guides symmetry-focused pretraining via four proxy tasks, including an explicit symmetry loss defined by and the cross-attention formula with , , . Pretraining on large MRI and CT datasets yields strong zero-shot, few-shot, and downstream performance on brain disease identification and lesion segmentation, achieving state-of-the-art results on benchmarks such as BraTS2021 and CQ500. The findings demonstrate the practical value of encoding anatomical symmetry in medical imaging, offering improved diagnostic accuracy and generalization while reducing the need for extensive labeled data; code is available at the authors' GitHub repository.

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.
Paper Structure (12 sections, 3 equations, 3 figures, 3 tables)

This paper contains 12 sections, 3 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Preprocssing
  4. Symmetry-Aware Cross-Attention (SACA) Module
  5. Symmetry-Aware Pretraining Proxies
  6. Experiments and Results
  7. Datasets and Downstream Tasks
  8. Implementation Details
  9. Results
  10. Conclusion
  11. Acknowledgments.
  12. Disclosure of Interests.

Figures (3)

  • Figure 1: Brain T1 images: (a) represents a healthy case, displaying symmetrical shape, structure, and image intensity. (b)-(d) depict patients with a brain tumor, Alzheimer's Disease, and focal epilepsy, respectively, and the noticeable structural asymmetry parts are circled out.
  • Figure 2: (a) The process of midway registration to align input images; (b) The framework applied to input sub-volumes and their sagittal symmetrical counterparts. This framework is augmented by the Symmetry-Aware Cross-Attention (SACA) module and is optimized through a composite loss function that integrates inpainting, rotation, contrastive, and symmetry losses.
  • Figure 3: Symmetry-Aware Cross-Attention (SACA) Module: (a) The computation of attention for each token within the SACA module; (b) Cross-attention calculation of SACA module structure.