Diversity-driven MG-MAE: Multi-granularity representation learning for non-salient object segmentation

Clinical Snapshot

Key Findings

• P Value: Not specified

• Effect Size: Statistically significant improvements (specific values not provided in abstract)

• Primary Outcome: Dice Similarity Coefficient (DSC) scores for non-salient object segmentation

• Nnt Or Sensitivity: Not applicable for technical validation study

• Confidence Interval: Not reported

Clinical Application

Requires computational infrastructure and technical expertise for implementation Relevant to Australian radiology departments and medical imaging centres; potential integration with existing PACS systems pending TGA software validation requirements Patients undergoing medical imaging for detection of microvasculature, accessory organs, and early-stage tumors

Abstract

Masked Autoencoders (MAEs) have grown increasingly prominent as a powerful self-supervised learning paradigm. They are capable of effectively leveraging inherent image prior information and are gaining traction in the field of medical image analysis. However, their application to feature representations of the non-salient objects, such as microvasculature, accessory organs, and early-stage tumors-is fundamentally limited by dimensional collapse problem, which diminishes feature diversity critical for non-salient structure discrimination. To address this, we propose a Multi-Granularity Masked Autoencoder (MG-MAE) framework for feature diversity learning: (1) We extend the conventional MAE into a multi-granularity framework, a global branch reconstructs global pixels, with a local branch recovering Histogram of Oriented Gradients (HOG) features, enabling hierarchical representation of both coarse-grained and fine-grained patterns; (2) Critically, in the local branch, a diversity-enhanced loss function incorporating Nuclear Norm Maximization (NNM) constraint to explicitly mitigate feature space collapse through orthogonal embedding regularization; and (3) A Dynamic Weight Adjustment (DWA) strategy that dynamically prioritizes hard-to-reconstruct regions via entropy-driven gradient modulation. Comprehensive evaluations across five clinical benchmarks-CCTA139, BTCV, LiTS, ACDC, and MSD Pancreas Tumour datasets-demonstrate that MG-MAE achieves statistically significant improvements in Dice Similarity Coefficient (DSC) scores for non-salient object segmentation, outperforming state-of-the-art methods. The code is available at https://github.com/zhangbbin/mgmae.

References

1. 1.Yu, C., Zhang, B., Xu, C., Ruan, D., Wang, R., Liu, H., Li, X., & Li, S. (2026). Diversity-driven MG-MAE: Multi-granularity representation learning for non-salient object segmentation. Medical Image Analysis, 103971. https://doi.org/10.1016/j.media.2026.103971