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ULFAC-Net: Ultra-Lightweight Fully Asymmetric Convolutional Network for Skin Lesion Segmentation
IEEE Journal of Biomedical and Health Informatics
|April 8, 2023
Summary
We developed ULFAC-Net, an ultralightweight network for skin lesion segmentation. This efficient model achieves competitive performance with minimal parameters, enabling deployment on resource-limited dermoscopic devices.
Area of Science:
- Medical image analysis
- Computer vision
- Artificial intelligence in dermatology
Background:
- Accurate skin lesion segmentation is crucial for diagnosis but challenging due to irregular shapes and noise.
- Current deep learning methods are computationally expensive, limiting their use in portable dermoscopic devices.
Purpose of the Study:
- To propose an ultralightweight fully asymmetric convolutional network (ULFAC-Net) for efficient skin lesion segmentation.
- To address the computational cost limitations of existing deep learning models for deployment on low-power devices.
Main Methods:
- Introduced Parallel Asymmetric Convolution (PAC) and Attentive PAC (Att-PAC) modules for feature extraction and enhancement.
- Developed a lightweight textual information submodule and an asymmetric encoder-decoder architecture.
- Validated ULFAC-Net on ISIC2018, ISBI2017, ISIC2016, and PH2 datasets.
Main Results:
- ULFAC-Net achieved competitive segmentation performance across multiple datasets.
- The proposed network has only 0.842 million parameters and 3.71 GFLOPs.
- Demonstrated effectiveness and robustness compared to state-of-the-art methods.
Conclusions:
- ULFAC-Net offers an efficient and effective solution for skin lesion segmentation.
- The ultralightweight design makes it suitable for deployment on dermoscopic devices with limited computational power.
