Abstract
Dermoscopic images segmentation is a key step in skin cancer diagnosis and analysis. Convolutional Neural Networks (CNNs) has achieved great success in various medical image segmentation tasks. However, continuous down-sampling operation brings network redundancy and loss of local details, and also shows great limitations in modeling of long-range relationships. Inversely, Transformer shows great potential in modeling global contexts. In this paper, we propose a novel segmentation network combining Transformers and CNNs (CTCNet) for Skin Lesions to improve the efficiency of the network in modeling the global context, while maintaining the control of the underlying details. Besides, a novel fusion technique - Two-stream Cascaded Feature Aggregation (TCFA) module, is constructed to integrate multi-level features from two branches efficiently. Moreover, we design a Multi-Scale Expansion-Aware (MSEA) module based on the convolution of feature perception and expansion, which can extract high-level features containing more abundant context information and further enhance the perception ability of network. CTCNet combines Transformers and CNNs in a parallel style, where both global dependency and low-level spatial details can be efficiently captured. Extensive experiments demonstrate that CTCNet achieves the better performance compared with state-of-the-art approaches.
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Acknowledgement
This research was supported by the National Natural Science Foundation of China (No. 61901537), Research funds for overseas students in Henan Province, China Postdoctoral Science Foundation (No. 2020M672274), Science and technology guiding project of China National Textile and Apparel Council (No. 2019059), Postdoctoral Research Sponsorship in Henan Province (No. 19030018), Program of Young backbone teachers in Zhongyuan University of Technology (No. 2019XQG04), Training Program of Young Master’s Supervisor in Zhongyuan University of Technology (No. SD202207).
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Wang, J., Li, B., Guo, X., Huang, J., Song, M., Wei, M. (2022). CTCNet: A Bi-directional Cascaded Segmentation Network Combining Transformers with CNNs for Skin Lesions. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13535. Springer, Cham. https://doi.org/10.1007/978-3-031-18910-4_18
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