Abstract
BackgroundProteins are of extremely vital importance in the human body, and no movement or activity can be performed without proteins. Currently, microscopy imaging technologies developed rapidly are employed to observe proteins in various cells and tissues. In addition, due to the complex and crowded cellular environments as well as various types and sizes of proteins, a considerable number of protein images are generated every day and cannot be classified manually. Therefore, an automatic and accurate method should be designed to properly solve and analyse protein images with mixed patterns.ResultsIn this paper, we first propose a novel customized architecture with adaptive concatenate pooling and “buffering” layers in the classifier part, which could make the networks more adaptive to training and testing datasets, and develop a novel hard sampler at the end of our network to effectively mine the samples from small classes. Furthermore, a new loss is presented to handle the label imbalance based on the effectiveness of samples. In addition, in our method, several novel and effective optimization strategies are adopted to solve the difficult training-time optimization problem and further increase the accuracy by post-processing.ConclusionOur methods outperformed the SOTA method of multi-labelled protein classification on the HPA dataset, GapNet-PL, by above 2% in the F1 score. Therefore, experimental results based on the test set split from the Human Protein Atlas dataset show that our methods have good performance in automatically classifying multi-class and multi-labelled high-throughput microscopy protein images.
Highlights
Proteins are of extremely vital importance in the human body, and no movement or activity can be performed without proteins
The experimental results demonstrate that our methods make the base models achieve higher F1 scores than other baseline approaches, including the SOTA method
When designing our network architectures, we proposed our customized layers, such as “buffering” layers and adaptive concatenate pooling (ACP) layers, which made our networks more adaptive to the task
Summary
Proteins are of extremely vital importance in the human body, and no movement or activity can be performed without proteins. Due to the complex and crowded cellular environments as well as various types and sizes of proteins, a considerable number of protein images are generated every day and cannot be classified manually. Proteins execute all kinds of functions in the human body within different types of cells, and proteins in various environments perform differently. An algorithm capable of classifying mixed patterns of proteins was needed to make the system “smarter”. The algorithm or method is expected to accurately and efficiently recognize multi-patterns that are mixed together among various types of cells. As the HPA project has provided adequate protein microscopy images, as shown, with annotations to feed a large neuron network, we decided to adopt the DNN method, which has good performance in classifying images As the HPA project has provided adequate protein microscopy images, as shown in Fig. 1, with annotations to feed a large neuron network, we decided to adopt the DNN method, which has good performance in classifying images
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
