Abstract
The segmentation and classification of different types of nuclei plays an important role in discriminating and diagnosing of the initiation, development, invasion, metastasis and therapeutic response of tumors of various organs. Recently, deep learning method based on attention mechanism has achieved good results in nuclei semantic segmentation. However, the design of attention module architecture relies heavily on the experience of researchers and a large number of experiments. Therefore, in order to avoid this manual design and achieve better performance, we propose a new Neural Architecture Search-based Spatial and Channel joint Attention Module (NAS-SCAM) to obtain better spatial and channel weighting effect. To the best of our knowledge, this is the first time to apply NAS to the attention mechanism. At the same time, we also use synchronous search strategy to search architectures independently for different attention modules in the same network structure. We verify the superiority of our methods over the state-of-the-art attention modules and networks in public dataset of MoNuSAC 2020. We make our code and model available at https://github.com/ZuhaoLiu/NAS-SCAM.
Z. Liu and H. Wang—Equal contribution.
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Acknowledgements
This work is supported by Sichuan Jiuzhou electric Group Co. Ltd, Sichuan, Mianyang, 621000, China, and National Natural Science Foundation of China under grant no. 81771921, and Glasgow College, University of Electronic Science and Technology of China.
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Liu, Z., Wang, H., Zhang, S., Wang, G., Qi, J. (2020). NAS-SCAM: Neural Architecture Search-Based Spatial and Channel Joint Attention Module for Nuclei Semantic Segmentation and Classification. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12261. Springer, Cham. https://doi.org/10.1007/978-3-030-59710-8_26
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