The proposed method can automatically extract the deep features embedded in cell atlas images and realize multi-label subcellular protein classification.
Jul 9, 2019 · This study proposed a high-performance recognition system for protein atlas classification based on deep learning, and it achieved an ...
This work aims to build an automatic recognition system for multi-label human protein atlas classification based on deep learning. Methods: In this work, an ...
A novel image-based multi-label HPA classification network (AMCNet) was proposed. •. The proposed system in this paper exhibits stronger classification ...
Background and objectives The multi-label Human Protein Atlas (HPA) classification can yield a better understanding of human diseases and help doctors to ...
AMC-Net: Asymmetric and multi-scale convolutional neural network for multi-label HPA classification. S Xiang, Q Liang, Y Hu, P Tang, G Coppola, D Zhang, W ...
2023. AMC-Net: Asymmetric and multi-scale convolutional neural network for multi-label HPA classification. S Xiang, Q Liang, Y Hu, P Tang, G Coppola, D Zhang ...
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Bibliographic details on AMC-Net: Asymmetric and multi-scale convolutional neural network for multi-label HPA classification.
AMC-Net: Asymmetric and multi-scale convolutional neural network for multi-label HPA classification. Comput. Methods Programs Biomed. 178: 275-287 (2019).
AMC-Net: Asymmetric and multi-scale convolutional neural network for multi-label HPA classification. PDF available through Get Fulltext Research.