Masked Mamba: An Efficient Self-Supervised Framework for Pathological Image Classification
Keywords: Pathological image classification, Mamba model, Self-supervised learning
Abstract: Extracting visual representations is a crucial challenge in the domain of computational histopathology. Considering the powerful strength of deep learning algorithms and the dearth of annotated samples, self-supervised learning presents itself as a compelling strategy to extract effective visual representations from unlabeled histopathology images. Although some self-supervised learning methods have been specifically proposed for histopathology image classification, most of them have certain drawbacks that may affect the functionality or representation capacity. In this work, we propose Masked Mamba, a novel self-supervised visual representation learning method tailored for histopathology images that can adequately extract local-global features. The proposed method consists of two stages: local perception positional encoding (LPPE) and directional Mamba vision backbone (DM). In addition, we use masked autoencoder (MAE) pretraining to unleashing directional Mamba vision backbone's potential. Masked Mamba makes good use of domain-specific knowledge and requires no side information, which means good rationality and versatility. Experimental results demonstrate the effectiveness and robustness of masked Mamba on common histopathology classification tasks. Furthermore, ablation studies prove that the local perception positional encoding and directional Mamba vision backbone in masked Mamba can complement and enhance each other.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 6954
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