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Pact-Net: Parallel CNNs and Transformers for medical image segmentation.
Weilin Chen1, Rui Zhang1, Yunfeng Zhang1
1School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, Shandong, 250014, China.
Computer Methods and Programs in Biomedicine
|September 10, 2023
Summary
This study introduces Pact-Net, a novel deep learning model combining CNNs and Transformers for improved skin lesion segmentation. Pact-Net effectively captures both local and global features, outperforming existing methods for enhanced clinical diagnosis.
Area of Science:
- Medical Image Analysis
- Computer Vision
- Machine Learning
Background:
- Skin lesion segmentation is crucial for clinical diagnosis but challenging due to low contrast and variable lesion characteristics.
- Traditional deep learning methods struggle with extracting global features, leading to segmentation inaccuracies like over- and under-segmentation.
Purpose of the Study:
- To develop an optimized skin image segmentation model that effectively integrates local and global features.
- To address the limitations of traditional convolutional neural networks (CNNs) in capturing global context for medical image segmentation.
Main Methods:
- Designed Parallel CNNs and Transformers for Medical Image Segmentation (Pact-Net), a dual-branch network capturing local and global features.
- Introduced a novel fusion module (CSMF) utilizing channel, spatial attention, and multi-scale mechanisms to integrate Transformer-extracted global information with CNN-extracted local features.
Main Results:
- Pact-Net achieved superior performance on ISIC 2016, ISIC 2017, and ISIC 2018 datasets, with accuracy rates of 86.95%, 79.31%, and 84.14%, respectively.
- Experiments on cell and polyp datasets demonstrated Pact-Net's robustness, learning, and generalization capabilities.
- Ablation studies confirmed the effectiveness of individual components within Pact-Net.
Conclusions:
- Pact-Net effectively leverages the strengths of CNNs and Transformers for enhanced skin lesion segmentation.
- The proposed model demonstrates state-of-the-art segmentation capabilities, offering significant potential to aid clinicians in disease diagnosis and treatment.
