Daily Arxiv

This is a page that curates AI-related papers published worldwide.
All content here is summarized using Google Gemini and operated on a non-profit basis.
Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation

Created by
  • Haebom

Author

Chao Deng, Xiaosen Li, Xiao Qin

Outline

This paper proposes a self-adaptive convolution module (SCM) that dynamically adjusts convolution kernel sizes based on the unique characteristics of the dataset to address the issue that nnUNet's automatic hyperparameter tuning does not account for internal hyperparameter tuning, resulting in poor generalization performance. This module is integrated into the Multi-Scale Convolution Bridge and Multi-Scale Amalgamation Decoder of MSA2-Net to effectively extract features at various scales and accurately capture details of organs of various sizes, resulting in accurate medical image segmentation results. We demonstrate excellent performance on the Synapse, ACDC, Kvasir, and ISIC2017 datasets, achieving Dice coefficients of 86.49%, 92.56%, 93.37%, and 92.98%, respectively.

Takeaways, Limitations

•
Takeaways:
◦
Improving medical image segmentation performance through dynamic tuning of network-internal hyperparameters.
◦
Achieving robust and accurate segmentation performance on diverse datasets through self-adaptive convolutional modules.
◦
Accurately capture details of organs of various sizes
◦
The superior performance of MSA2-Net is verified through various datasets.
•
Limitations:
◦
Lack of analysis of the computational cost and memory usage of the proposed module.
◦
More extensive comparative experiments with other state-of-the-art medical image segmentation models are needed.
◦
Further validation of the generalization performance of self-adaptive convolutional modules is needed.
◦
Lack of consideration for the possibility of overfitting to specific datasets
👍
Made with Slashpage