AMC-Net: Asymmetric and multi-scale convolutional neural network for multi-label HPA classification. (September 2019) - Xiang, Shao; Liang, Qiaokang; Hu, Yucheng; Tang, Pen; Coppola, Gianmarc; Zhang, Dan; Sun, Wei
Highlights: The ML technology approaches have been applied in the multi-label HPA classification. A novel image-based multi-label HPA classification network (AMCNet) was proposed. The proposed system in this paper exhibits stronger classification performance and wider scope of species and diseases compared to the state-of-the-art with a lower computation cost. To the best of our knowledge, few research teams have presented a significant method to handle the multi-label subcellular protein classification task. The proposed method can automatically extract the deep features embedded in cell atlas images and realize multi-label subcellular protein classification. Abstract: Background and objectives: The multi-label Human Protein Atlas (HPA) classification can yield a better understanding of human diseases and help doctors to enhance the automatic analysis of biomedical images. The existing automatic protein recognition methods have been limited to single pattern. Therefore, an automatic multi-label human protein atlas recognition system with satisfactory performance should be conducted. 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 automatic feature extraction and multi-label classification framework is proposed. Specifically, an asymmetric and multi-scale convolutional neural network is designed for HPA classification. Furthermore, this work introduces a combined loss that consists of the binary cross-entropy and F1-score losses to improve identification performance. Results: Rigorous experiments are conducted to estimate the proposed system. In particular, unlike the current automatic identification systems, which focus on a limited number of patterns, the proposed method is capable of classifying mixed patterns of proteins in microscope images and can handle the subcellular multi-label protein classification task including 28 subcellular localization patterns. The proposed framework based on deep convolutional neural network outperformed the existing approaches with a F1-score of 0.823, which illustrates the robustness and effectiveness of the proposed system. Conclusion: This study proposed a high-performance recognition system for protein atlas classification based on deep learning, and it achieved an automatic multi-label human protein atlas identification framework with superior performance than previous studies.