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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, 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: 0000-0002-0544-8272
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. Mathematical Institute of Serbian Academy of Sciences and Arts, Belgrade, Serbia. (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, 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. Vironova AB, Stockholm, Sweden. (Quantitative Microscopy)
Karolinska Institute.
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2019 (English)In: Computer Vision: ECCV 2018 Workshops, Cham: Springer, 2019, Vol. VI, p. 465-475Conference 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
Cham: Springer, 2019. Vol. VI, p. 465-475
Series
Lecture Notes in Computer Sciences (LNCS), ISSN 0302-9743, E-ISSN 1611-3349 ; 11134
Keywords [en]
Texture Analysis, Machine learning, Glomerulus detection, Transmission Electron Microscopy, Convolutional Neural Networks, Local binary patterns, Digital pathology
National Category
Computer Vision and Robotics (Autonomous Systems) Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-368015DOI: 10.1007/978-3-030-11024-6_36ISI: 000594200000036ISBN: 978-3-030-11023-9 (print)ISBN: 978-3-030-11024-6 (electronic)OAI: oai:DiVA.org:uu-368015DiVA, id: diva2:1267435
Conference
European Conference on Computer Vision - ECCV 2018, 8-14 September, Munich, Germany
Funder
Vinnova, 2016-02329Vinnova, 2017-02447Available from: 2018-12-03 Created: 2018-12-03 Last updated: 2023-04-16Bibliographically approved
In thesis
1. Representation Learning and Information Fusion: Applications in Biomedical Image Processing
Open this publication in new window or tab >>Representation Learning and Information Fusion: Applications in Biomedical Image Processing
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In recent years Machine Learning and in particular Deep Learning have excelled in object recognition and classification tasks in computer vision. As these methods extract features from the data itself by learning features that are relevant for a particular task, a key aspect of this remarkable success is the amount of data on which these methods train. Biomedical applications face the problem that the amount of training data is limited. In particular, labels and annotations are usually scarce and expensive to obtain as they require biological or medical expertise. One way to overcome this issue is to use additional knowledge about the data at hand. This guidance can come from expert knowledge, which puts focus on specific, relevant characteristics in the images, or geometric priors which can be used to exploit the spatial relationships in the images. This thesis presents machine learning methods for visual data that exploit such additional information and build upon classic image processing techniques, to combine the strengths of both model- and learning-based approaches. The thesis comprises five papers with applications in digital pathology. Two of them study the use and fusion of texture features within convolutional neural networks for image classification tasks. The other three papers study rotational equivariant representation learning, and show that learned, shared representations of multimodal images can be used for multimodal image registration and cross-modality image retrieval.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2023. p. 85
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 2266
Keywords
Representation Learning, Texture Descriptors, Equivariant Neural Networks, Contrastive Learning, Image Classification, Image Registration, Image Retrieval, Digital Pathology
National Category
Computer Vision and Robotics (Autonomous Systems) Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-500386 (URN)978-91-513-1802-8 (ISBN)
Public defence
2023-06-12, Polhemsalen, 10134, Ångström, Lägerhyddsvägen 1, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2023-05-16 Created: 2023-04-16 Last updated: 2023-05-16

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Wetzer, ElisabethLindblad, JoakimSintorn, Ida-MariaSladoje, Natasa

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Citation style
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