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Publications (6 of 6) Show all publications
Gay, J., Harlin, H., Wetzer, E., Lindblad, J. & Sladoje, N. (2019). Oral Cancer Detection: A Comparison of Texture Focused Deep Learning Approaches. In: Proceedings of the Swedish Society for Automated Image Analysis (SSBA): . Paper presented at Symposium on Image Analysis, Göteborg, Sweden, March 2019.
Open this publication in new window or tab >>Oral Cancer Detection: A Comparison of Texture Focused Deep Learning Approaches
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2019 (English)In: Proceedings of the Swedish Society for Automated Image Analysis (SSBA), 2019Conference paper, Oral presentation with published abstract (Other academic)
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-398345 (URN)
Conference
Symposium on Image Analysis, Göteborg, Sweden, March 2019
Available from: 2019-12-04 Created: 2019-12-04 Last updated: 2019-12-04
Gay, J., Harlin, H., Wetzer, E., Lindblad, J. & Sladoje, N. (2019). Texture-based oral cancer detection: A performance analysis of deep learning approaches.. In: 3rd NEUBIAS Conference: . Paper presented at 3rd NEUBIAS Conference. Luxembourg
Open this publication in new window or tab >>Texture-based oral cancer detection: A performance analysis of deep learning approaches.
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2019 (Swedish)In: 3rd NEUBIAS Conference, Luxembourg, 2019Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

Early stage cancer detection is essential for reducing cancer mortality. Screening programs such as that for cervical cancer are highly effective in preventing advanced stage cancers. One obstacle to the introduction of screening for other cancer types is the cost associated with manual inspection of the resulting cell samples. Computer assisted image analysis of cytology slides may offer a significant reduction of these costs. We are particularly interested in detection of cancer of the oral cavity, being one of the most common malignancies in the world, with an increasing tendency of incidence among young people. Due to the non-invasive accessibility of the oral cavity, automated detection may enable screening programs leading to early diagnosis and treatment.It is well known that variations in the chromatin texture of the cell nucleus are an important diagnostic feature. With an aim to maximize reliability of an automated cancer detection system for oral cancer detection, we evaluate three state of the art deep convolutional neural network (DCNN) approaches which are specialized for texture analysis. A powerful tool for texture description are local binary patterns (LBPs); they describe the pattern of variations in intensity between a pixel and its neighbours, instead of using the image intensity values directly. A neural network can be trained to recognize the range of patterns found in different types of images. Many methods have been proposed which either use LBPs directly, or are inspired by them, and show promising results on a range of different image classification tasks where texture is an important discriminative feature.We evaluate multiple recently published deep learning-based texture classification approaches: two of them (referred to as Model 1, by Juefei-Xu et al. (CVPR 2017); Model 2, by Li et al. (2018)) are inspired by LBP texture descriptors, while the third (Model 3, by Marcos et al. (ICCV 2017)), based on Rotation Equivariant Vector Field Networks, aims at preserving fine textural details under rotations, thus enabling a reduced model size. Performances are compared with state-of-the-art results on the same dataset, by Wieslander et al. (CVPR 2017), which are based on ResNet and VGG architectures. Furthermore a fusion of DCNN with LBP maps as in Wetzer et al. (Bioimg. Comp. 2018) is evaluated for comparison. Our aim is to explore if focus on texture can improve CNN performance.Both of the methods based on LBPs exhibit higher performances (F1-score for Model 1: 0.85; Model 2: 0.83) than what is obtained by using CNNs directly on the greyscale data (VGG: 0.78, ResNet: 0.76). This clearly demonstrates the effectiveness of LBPs for this type of image classification task. The approach based on rotation equivariant networks stays behind in performance (F1-score for Model 3: 0.72), indicating that this method may be less appropriate for classifying single-cell images.

Place, publisher, year, edition, pages
Luxembourg: , 2019
National Category
Computer and Information Sciences
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-398150 (URN)
Conference
3rd NEUBIAS Conference
Available from: 2019-12-02 Created: 2019-12-02 Last updated: 2019-12-04
Wetzer, E., Lindblad, J., Sintorn, I.-M., Hultenby, K. & Sladoje, N. (2019). Towards automated multiscale Glomeruli detection and analysis in TEM by fusion of CNN and LBP maps. In: 3rd NEUBIAS Conference: . Paper presented at 3rd NEUBIAS Conference. Luxembourg
Open this publication in new window or tab >>Towards automated multiscale Glomeruli detection and analysis in TEM by fusion of CNN and LBP maps
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2019 (English)In: 3rd NEUBIAS Conference, Luxembourg, 2019Conference paper, Oral presentation with published abstract (Other academic)
Abstract [en]

Glomeruli are special structures in kidneys which filter the plasma volume from metabolic waste.Podocytes are cells that wrap around the capillaries of the Glomerulus. They take an active role in the renal filtration by preventing plasma proteins from entering the urinary ultrafiltrate through slits between so called foot processes. A number of diseases, such as minimal change disease, systemic lupus and diabetic nephropathy, can affect the glomerulus and have serious impact on the kidneys and their function.When the resolution of optical microscopy is insufficient for a diagnosis, it is necessary to thoroughly examine the morphology of the podocytes in transmission electron microscopy (TEM). This includes measuring the size and shape of the foot processes, the thickness and overall morphology of the Glomerulus Base Membrane (GBM), and the number of slits along the GBM.The high resolution of TEM images produces large amounts of data and requires long acquisition time, which makes automated imaging and Glomerulus detection a desired option. We present a multi-step and multi-scale approach to automatically detect Glomeruli and subsequently foot processes by using convolutional neural networks (CNN). Previously, texture information in the form of local binary patterns (LBPs) has shown great success in Glomerulus detection in different modalities other than TEM. This motivates our approach to explore different methods to incorporate LBPs in CNN training to enhance the performance over training exclusively on intensity images. We use a modified approximation of the Earth mover’s distance to define dissimilarities between the initially unordered binary codes resulting from pixel-wise LBP computations.Multidimensional scaling based on those dissimilarities can be applied to compute LBP maps which are suitable as CNN input. We explore the effect of different radii and dimensions for the LBP maps generation, as well as the impact of early, mid and late fusion of intensity and texture information input. We compare the performance of ResNet50 and VGG16-like architectures. Furthermore we provide comparison of transfer learning of networks pretrained on ImageNet, as well as on a publicly available SEM database, a network architecture in which convolutional layers are replaced by local binary convolutional layers, as well as ‘classic’ methods such as SVM or 1-NN classification based on LBP histograms.We show that for Glomerulus detection, where texture is a main discriminative feature, CNN training on the texture based input provides complementary information not learned by the network on the intensity images and mid and late fusion can boost performance. In foot process detection, in which the scale shifts the focus from texture to morphology, the performance also benefits by the handcrafted texture features, though to a lesser extent than for the larger scale Glomerulus detection.

Place, publisher, year, edition, pages
Luxembourg: , 2019
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-398148 (URN)
Conference
3rd NEUBIAS Conference
Available from: 2019-12-02 Created: 2019-12-02 Last updated: 2019-12-04
Wetzer, E. & Lohninger, H. (2018). Image Processing using Color SpaceModels for Forensic Fiber Detection. In: IFAC PapersOnLine: . Paper presented at 9th Vienna International Conference on Mathematical Modelling, Vienna, Austria, Feb 21-23, 2018 (pp. 445-450). , 51
Open this publication in new window or tab >>Image Processing using Color SpaceModels for Forensic Fiber Detection
2018 (English)In: IFAC PapersOnLine, 2018, Vol. 51, p. 445-450Conference paper, Published paper (Refereed)
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:uu:diva-368017 (URN)10.1016/j.ifacol.2018.03.076 (DOI)000435693000077 ()
Conference
9th Vienna International Conference on Mathematical Modelling, Vienna, Austria, Feb 21-23, 2018
Available from: 2018-12-03 Created: 2018-12-03 Last updated: 2018-12-12Bibliographically approved
Wetzer, E., Lindblad, J., Sintorn, I.-M., Hultenby, K. & Sladoje, N. (2018). Towards automated multiscale imaging and analysis in TEM: Glomeruli detection by fusion of CNN and LBP maps. In: Swedish Symposium on Deep Learning: . Paper presented at 2nd Swedish Symposium on Deep Learning, 5-6 September, 2018,Göteborg, Sweden.
Open this publication in new window or tab >>Towards automated multiscale imaging and analysis in TEM: Glomeruli detection by fusion of CNN and LBP maps
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2018 (English)In: Swedish Symposium on Deep Learning, 2018Conference paper, Oral presentation with published abstract (Other academic)
Keywords
Machine learning
National Category
Medical Image Processing Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-368016 (URN)
Conference
2nd Swedish Symposium on Deep Learning, 5-6 September, 2018,Göteborg, Sweden
Available from: 2018-12-03 Created: 2018-12-03 Last updated: 2019-03-14Bibliographically approved
Wetzer, E., Lindblad, J., Sintorn, I.-M., Hultenby, K. & Sladoje, N. (2018). Towards automated multiscale imaging and analysis in TEM: Glomerulus detection by fusion of CNN and LBP maps. In: Workshop on BioImage Computing @ ECCV 2018: . Paper presented at European Conference on Computer Vision - ECCV 2018, 8-14 September, Munich, Germany (pp. 465-475). Springer
Open this publication in new window or tab >>Towards automated multiscale imaging and analysis in TEM: Glomerulus detection by fusion of CNN and LBP maps
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2018 (English)In: Workshop on BioImage Computing @ ECCV 2018, Springer, 2018, 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
Springer, 2018
Series
Lecture Notes in Computer Sciences (LNCS), ISSN 0302-9743 ; 11134
Keywords
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:nbn:se:uu:diva-368015 (URN)10.1007/978-3-030-11024-6_36 (DOI)
Conference
European Conference on Computer Vision - ECCV 2018, 8-14 September, Munich, Germany
Available from: 2018-12-03 Created: 2018-12-03 Last updated: 2019-12-03
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0544-8272

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