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  • 1.
    Gay, Jo
    et al.
    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.
    Harlin, Hugo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Wetzer, Elisabeth
    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.
    Lindblad, Joakim
    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.
    Sladoje, Natasa
    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.
    Oral Cancer Detection: A Comparison of Texture Focused Deep Learning Approaches2019In: Proceedings of the Swedish Society for Automated Image Analysis (SSBA), 2019Conference paper (Other academic)
  • 2.
    Gay, Jo
    et al.
    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.
    Harlin, Hugo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Wetzer, Elisabeth
    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.
    Lindblad, Joakim
    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.
    Sladoje, Natasa
    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.
    Texture-based oral cancer detection: A performance analysis of deep learning approaches.2019In: 3rd NEUBIAS Conference, Luxembourg, 2019Conference paper (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.

  • 3.
    Wetzer, Elisabeth
    et al.
    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.
    Lindblad, Joakim
    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.
    Sintorn, Ida-Maria
    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.
    Hultenby, Kjell
    Sladoje, Natasa
    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.
    Towards automated multiscale Glomeruli detection and analysis in TEM by fusion of CNN and LBP maps2019In: 3rd NEUBIAS Conference, Luxembourg, 2019Conference paper (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.

  • 4.
    Wetzer, Elisabeth
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Sintorn, Ida-Maria
    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.
    Hultenby, Kjell
    Karolinska Institute.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Towards automated multiscale imaging and analysis in TEM: Glomeruli detection by fusion of CNN and LBP maps2018In: Swedish Symposium on Deep Learning, 2018Conference paper (Other academic)
  • 5.
    Wetzer, Elisabeth
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Sintorn, Ida-Maria
    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.
    Hultenby, Kjell
    Karolinska Institute.
    Sladoje, Natasa
    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.
    Towards automated multiscale imaging and analysis in TEM: Glomerulus detection by fusion of CNN and LBP maps2018In: Workshop on BioImage Computing @ ECCV 2018, Springer, 2018, p. 465-475Conference 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.

  • 6.
    Wetzer, Elisabeth
    et al.
    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. Vienna University of Technology.
    Lohninger, Hans
    Vienna University of Technology.
    Image Processing using Color SpaceModels for Forensic Fiber Detection2018In: IFAC PapersOnLine, 2018, Vol. 51, p. 445-450Conference paper (Refereed)
1 - 6 of 6
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  • nn-NO
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