Abstract
Skin melanoma is one of the most malignant tumors. In recent years, its incidence rate and mortality showed a high growth trend. Early detection and segmentation of skin lesions are vital in timely diagnosis and treatment. As the low contrast of lesion regions and high similarity in terms of appearance, skin lesion segmentation still remains a challenging work. Most of the segmentation methods use single-scale feature fusion, leading to the blur effect on the boundary. In this paper, we propose a new segmentation framework named Intensive Atrous Spatial Transformer Network (IASTrans-Net), which is based on a core module Intensive Atrous Spatial Pyramid Pooling (IASPP). The introduced IASPP can obtain valid features by using multi-scale feature fusion and channel attention. On the one hand, we employ atrous convolution with different dilation rates for multi-scale information extraction, ensuring that the effective information of each channel is obtained. On the other hand, channel attention is used to screen features, which can enable the network to effectively identify targets without increasing the training complexity. The experimental results show that the proposed IASTrans-Net has achieved good results in ISIC2017 and ISIC2018 datasets, surpassing most of the current mainstream methods.
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Acknowledgements
This work was supported in part by the Special Project of Central Government Guiding Local Science and Technology Development (Grant No. 2021JH6/10500140), Program for the Liaoning Distinguished Professor, Program for Innovative Research Team in University of Liaoning Province (Grant No. LT2020015), the Support Plan for Key Field Innovation Team of Dalian (Grant No. 2021RT06), the Science and Technology Innovation Fund of Dalian (Grant No. 2020JJ25CY001), the Support Plan for Leading Innovation Team of Dalian University (Grant No. XLJ202010), Program for the Liaoning Province Doctoral Research Starting Fund (Grant No. 2022-BS-336).
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Liu, X., Fan, W., Zhou, D. (2022). Skin Lesion Segmentation via Intensive Atrous Spatial Transformer. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13471. Springer, Cham. https://doi.org/10.1007/978-3-031-19208-1_2
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