A skin lesion segmentation method based on dynamic convolution and attention mechanism

Zhengyi Fu; Shibao Sun

doi:10.1117/12.3049782

8 November 2024 A skin lesion segmentation method based on dynamic convolution and attention mechanism

Zhengyi Fu, Shibao Sun

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 1341640 (2024) https://doi.org/10.1117/12.3049782
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

Lesion area segmentation is usually an important means for medical images to locate diseases, analyze and identify, and extract key discriminatory features for diagnosis. In this paper, in order to solve the problems of inconsistent size of skin cancer lesions, fuzzy lesion subjects, and complex boundary features, an Enhanced Dynamic Convolution Module (ECCM) is proposed, which is composed of Dynamic Convolution and ParNet Attention (PNA) modules in serial mode, in which the CondConv module will replace the static convolution module in the network, which can promote the learning of discriminative features and effectively improve the quality of model feature extraction. PNA, on the other hand, reduces the loss of detailed information in the lesion area through multi-level feature fusion. Experiments show that the proposed method can not only improve the extraction ability of model features, but also effectively improve the phenomenon of inaccurate edge segmentation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhengyi Fu and Shibao Sun "A skin lesion segmentation method based on dynamic convolution and attention mechanism", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 1341640 (8 November 2024); https://doi.org/10.1117/12.3049782

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KEYWORDS

Image segmentation

Convolution

Data modeling

Performance modeling

Skin

Feature extraction

Education and training

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