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Reducing the U-Net size for practical scenarios: Virus recognition in electron microscopy images
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.ORCID iD: 0000-0002-6148-5174
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Vironova AB, Gavlegatan 22, Stockholm, Sweden.
2019 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 178, p. 31-39Article in journal (Refereed) Published
Abstract [en]

Background and objective: Convolutional neural networks (CNNs) offer human experts-like performance and in the same time they are faster and more consistent in their prediction. However, most of the proposed CNNs require an expensive state-of-the-art hardware which substantially limits their use in practical scenarios and commercial systems, especially for clinical, biomedical and other applications that require on-the-fly analysis. In this paper, we investigate the possibility of making CNNs lighter by parametrizing the architecture and decreasing the number of trainable weights of a popular CNN: U-Net. Methods: In order to demonstrate that comparable results can be achieved with substantially less trainable weights than the original U-Net we used a challenging application of a pixel-wise virus classification in Transmission Electron Microscopy images with minimal annotations (i.e. consisting only of the virus particle centers or centerlines). We explored 4 U-Net hyper-parameters: the number of base feature maps, the feature maps multiplier, the number of the encoding-decoding levels and the number of feature maps in the last 2 convolutional layers. Results: Our experiments lead to two main conclusions: 1) the architecture hyper-parameters are pivotal if less trainable weights are to be used, and 2) if there is no restriction on the trainable weights number using a deeper network generally gives better results. However, training larger networks takes longer, typically requires more data and such networks are also more prone to overfitting. Our best model achieved an accuracy of 82.2% which is similar to the original U-Net while using nearly 4 times less trainable weights (7.8 M in comparison to 31.0 M). We also present a network with < 2M trainable weights that achieved an accuracy of 76.4%. Conclusions: The proposed U-Net hyper-parameter exploration can be adapted to other CNNs and other applications. It allows a comprehensive CNN architecture designing with the aim of a more efficient trainable weight use. Making the networks faster and lighter is crucial for their implementation in many practical applications. In addition, a lighter network ought to be less prone to over-fitting and hence generalize better. (C) 2019 Published by Elsevier B.V.

Place, publisher, year, edition, pages
ELSEVIER IRELAND LTD , 2019. Vol. 178, p. 31-39
Keywords [en]
Deep learning, Hyper parameter optimization, Hardware integration, Transmission Electron Microscopy
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:uu:diva-393644DOI: 10.1016/j.cmpb.2019.05.026ISI: 000480432000004PubMedID: 31416558OAI: oai:DiVA.org:uu-393644DiVA, id: diva2:1354780
Funder
Swedish Research Council, 2014-6075Available from: 2019-09-26 Created: 2019-09-26 Last updated: 2019-10-09Bibliographically approved
In thesis
1. Image and Data Analysis for Biomedical Quantitative Microscopy
Open this publication in new window or tab >>Image and Data Analysis for Biomedical Quantitative Microscopy
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis presents automatic image and data analysis methods to facilitate and improve microscopy-based research and diagnosis. New technologies and computational tools are necessary for handling the ever-growing amounts of data produced in life science. The thesis presents methods developed in three projects with different biomedical applications.

In the first project, we analyzed a large high-content screen aimed at enabling personalized medicine for glioblastoma patients. We focused on capturing drug-induced cell-cycle disruption in fluorescence microscopy images of cancer cell cultures. Our main objectives were to identify drugs affecting the cell-cycle and to increase the understanding of different drugs’ mechanisms of action.  Here we present tools for automatic cell-cycle analysis and identification of drugs of interest and their effective doses.

In the second project, we developed a feature descriptor for image matching. Image matching is a central pre-processing step in many applications. For example, when two or more images must be matched and registered to create a larger field of view or to analyze differences and changes over time. Our descriptor is rotation-, scale-, and illumination-invariant and it has a short feature vector which makes it computationally attractive. The flexibility to combine it with any feature detector and the customization possibility make it a very versatile tool.

In the third project, we addressed two general problems for bridging the gap between deep learning method development and their use in practical scenarios. We developed a method for convolutional neural network training using minimally annotated images. In many biomedical applications, the objects of interest cannot be accurately delineated due to their fuzzy shape, ambiguous morphology, image quality, or the expert knowledge and time it requires. The minimal annotations, in this case, consist of center-points or centerlines of target objects of approximately known size. We demonstrated our training method in a challenging application of a multi-class semantic segmentation of viruses in transmission electron microscopy images. We also systematically explored the influence of network architecture hyper-parameters on its size and performance and show the possibility to substantially reduce the size of a network without compromising its performance.

All methods in this thesis were designed to work with little or no input from biomedical experts but of course, require fine-tuning for new applications. The usefulness of the tools has been demonstrated by collaborators and other researchers and has inspired further development of related algorithms.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 63
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1864
Keywords
high-content screening, drug selection, DNA content histogram, manual image annotation, deep learning, convolutional neural networks, hardware integration
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-394709 (URN)978-91-513-0771-8 (ISBN)
Public defence
2019-12-12, 2446, ITC, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2019-11-20 Created: 2019-10-09 Last updated: 2019-11-27

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Matuszewski, Damian J.Sintorn, Ida-Maria

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