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Towards automated multiscale imaging and analysis in TEM: Glomerulus detection by fusion of CNN and LBP maps
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. (Methods in Image Data Analysis)ORCID iD: 0000-0002-0544-8272
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. (Methods in Image Data Analysis)ORCID iD: 0000-0001-7312-8222
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. Uppsala University, Science for Life Laboratory, SciLifeLab. (Quantitative Microscopy)
Karolinska Institute.
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2018 (English)In: Workshop on BioImage Computing @ ECCV 2018, Springer, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Glomerulal structures in kidney tissue have to be analysed at a nanometer scale for several medical diagnoses. They are therefore commonly imaged using Transmission Electron Microscopy. The high resolution produces large amounts of data and requires long acquisition time, which makes automated imaging and glomerulus detection a desired option. This paper presents a deep learning approach for Glomerulus detection, using two architectures, VGG16 (with batch normalization) and ResNet50. To enhance the performance over training based only on intensity images, multiple approaches to fuse the input with texture information encoded in local binary patterns of different scales have been evaluated. The results show a consistent improvement in Glomerulus detection when fusing texture-based trained networks with intensity-based ones at a late classification stage.

Place, publisher, year, edition, pages
Springer, 2018.
Keywords [en]
Texture Analysis, Convolutional Neural Networks, Machine learning
National Category
Computer Vision and Robotics (Autonomous Systems) Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-368015OAI: oai:DiVA.org:uu-368015DiVA, id: diva2:1267435
Conference
European Conference on Computer Vision - ECCV 2018
Note

Paper in print

Available from: 2018-12-03 Created: 2018-12-03 Last updated: 2018-12-04

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Authority records BETA

Wetzer, ElisabethLindblad, JoakimSintorn, Ida-MariaSladoje, Natasa

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Wetzer, ElisabethLindblad, JoakimSintorn, Ida-MariaSladoje, Natasa
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Division of Visual Information and InteractionComputerized Image Analysis and Human-Computer InteractionScience for Life Laboratory, SciLifeLab
Computer Vision and Robotics (Autonomous Systems)Medical Image Processing

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