# MPS-AMS: Masked Patches Selection and Adaptive Masking Strategy Based Self-Supervised Medical Image Segmentation

## TL;DR

This paper introduces MPS-AMS, a novel self-supervised medical image segmentation method that uses masked patches selection and adaptive masking to enhance lesion representation and improve segmentation performance.

## Contribution

The paper proposes a new self-supervised approach with masked patches selection and adaptive masking specifically designed for medical image segmentation, addressing limitations of existing methods.

## Key findings

- Significantly outperforms state-of-the-art self-supervised methods on three medical datasets.
- Effectively captures lesion information through masked patches selection.
- Adaptive masking strategy improves mutual information and segmentation accuracy.

## Abstract

Existing self-supervised learning methods based on contrastive learning and masked image modeling have demonstrated impressive performances. However, current masked image modeling methods are mainly utilized in natural images, and their applications in medical images are relatively lacking. Besides, their fixed high masking strategy limits the upper bound of conditional mutual information, and the gradient noise is considerable, making less the learned representation information. Motivated by these limitations, in this paper, we propose masked patches selection and adaptive masking strategy based self-supervised medical image segmentation method, named MPS-AMS. We leverage the masked patches selection strategy to choose masked patches with lesions to obtain more lesion representation information, and the adaptive masking strategy is utilized to help learn more mutual information and improve performance further. Extensive experiments on three public medical image segmentation datasets (BUSI, Hecktor, and Brats2018) show that our proposed method greatly outperforms the state-of-the-art self-supervised baselines.