| 1. |
- Andersson, Axel, et al.
(författare)
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Transcriptome-Supervised Classification of Tissue Morphology Using Deep Learning
- 2020
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Ingår i: IEEE 17th International Symposium on Biomedical Imaging (ISBI). - 9781538693308 - 9781538693315 ; , s. 1630-1633
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Konferensbidrag (refereegranskat)abstract
- Deep learning has proven to successfully learn variations in tissue and cell morphology. Training of such models typically relies on expensive manual annotations. Here we conjecture that spatially resolved gene expression, e.i., the transcriptome, can be used as an alternative to manual annotations. In particular, we trained five convolutional neural networks with patches of different size extracted from locations defined by spatially resolved gene expression. The network is trained to classify tissue morphology related to two different genes, general tissue, as well as background, on an image of fluorescence stained nuclei in a mouse brain coronal section. Performance is evaluated on an independent tissue section from a different mouse brain, reaching an average Dice score of 0.51. Results may indicate that novel techniques for spatially resolved transcriptomics together with deep learning may provide a unique and unbiased way to find genotype phenotype relationships
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| 2. |
- Gupta, Anindya, et al.
(författare)
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Weakly-supervised prediction of cell migration modes in confocal microscopy images using bayesian deep learning
- 2020
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Ingår i: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). - 9781538693308 - 9781538693315 ; , s. 1626-1629
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Konferensbidrag (refereegranskat)abstract
- Cell migration is pivotal for their development, physiology and disease treatment. A single cell on a 2D surface can utilize continuous or discontinuous migration modes. To comprehend the cell migration, an adequate quantification for single cell-based analysis is crucial. An automatized approach could alleviate tedious manual analysis, facilitating large-scale drug screening. Supervised deep learning has shown promising outcomes in computerized microscopy image analysis. However, their implication is limited due to the scarcity of carefully annotated data and uncertain deterministic outputs. We compare three deep learning models to study the problem of learning discriminative morphological representations using weakly annotated data for predicting the cell migration modes. We also estimate Bayesian uncertainty to describe the confidence of the probabilistic predictions. Amongst three compared models, DenseNet yielded the best results with a sensitivity of 87.91%±13.22 at a false negative rate of 1.26%±4.18.
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| 3. |
- Pielawski, Nicolas, et al.
(författare)
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In Silico Prediction of Cell Traction Forces
- 2020
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Ingår i: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). - 9781538693308 - 9781538693315 ; , s. 877-881
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Konferensbidrag (refereegranskat)abstract
- Traction Force Microscopy (TFM) is a technique used to determine the tensions that a biological cell conveys to the underlying surface. Typically, TFM requires culturing cells on gels with fluorescent beads, followed by bead displacement calculations. We present a new method allowing to predict those forces from a regular fluorescent image of the cell. Using Deep Learning, we trained a Bayesian Neural Network adapted for pixel regression of the forces and show that it generalises on different cells of the same strain. The predicted forces are computed along with an approximated uncertainty, which shows whether the prediction is trustworthy or not. Using the proposed method could help estimating forces when calculating non-trivial bead displacements and can also free one of the fluorescent channels of the microscope. Code is available at https://github.com/wahlby-lab/InSilicoTFM.
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| 4. |
- Wetzer, Elisabeth, et al.
(författare)
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When Texture Matters : Texture-Focused Cnns Outperform General Data Augmentation and Pretraining in Oral Cancer Detection
- 2020
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Ingår i: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020). - : IEEE. - 9781538693308 - 9781538693315 ; , s. 517-521
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Konferensbidrag (refereegranskat)abstract
- Early detection is essential to reduce cancer mortality. Oral cancer could be subject to screening programs (similar as for cervical cancer) by collecting Pap smear samples at any dentist visit. However, manual analysis of the resulting massive amount of data is prohibitively costly. Convolutional neural networks (CNNs) have shown promising results in discriminating between cancerous and non-cancerous cells, which enables efficient automated processing of cancer screening data. We investigate different CNN architectures which explicitly aim to utilize texture information, for cytological cancer classification, motivated by studies showing that chromatin texture is among the most important discriminative features for that purpose. Results show that CNN classifiers inspired by Local Binary Patterns (LBPs) achieve better performance than general purpose CNNs. This holds also when different levels of general data augmentation, as well as pre-training, are considered.
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| 5. |
- Abramian, David, 1992-, et al.
(författare)
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Anatomically Informed Bayesian Spatial Priors for FMRI Analysis
- 2020
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Ingår i: ISBI 2020. - : IEEE. - 9781538693308
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Konferensbidrag (refereegranskat)abstract
- Existing Bayesian spatial priors for functional magnetic resonance imaging (fMRI) data correspond to stationary isotropic smoothing filters that may oversmooth at anatomical boundaries. We propose two anatomically informed Bayesian spatial models for fMRI data with local smoothing in each voxel based on a tensor field estimated from a T1-weighted anatomical image. We show that our anatomically informed Bayesian spatial models results in posterior probability maps that follow the anatomical structure.
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| 6. |
- Abramian, David, 1992-, et al.
(författare)
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Improved Functional MRI Activation Mapping in White Matter Through Diffusion-Adapted Spatial Filtering
- 2020
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Ingår i: ISBI 2020. - : IEEE. - 1945-8452 .- 1945-7928. - 9781538693308
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Konferensbidrag (refereegranskat)abstract
- Brain activation mapping using functional MRI (fMRI) based on blood oxygenation level-dependent (BOLD) contrast has been conventionally focused on probing gray matter, the BOLD contrast in white matter having been generally disregarded. Recent results have provided evidence of the functional significance of the white matter BOLD signal, showing at the same time that its correlation structure is highly anisotropic, and related to the diffusion tensor in shape and orientation. This evidence suggests that conventional isotropic Gaussian filters are inadequate for denoising white matter fMRI data, since they are incapable of adapting to the complex anisotropic domain of white matter axonal connections. In this paper we explore a graph-based description of the white matter developed from diffusion MRI data, which is capable of encoding the anisotropy of the domain. Based on this representation we design localized spatial filters that adapt to white matter structure by leveraging graph signal processing principles. The performance of the proposed filtering technique is evaluated on semi-synthetic data, where it shows potential for greater sensitivity and specificity in white matter activation mapping, compared to isotropic filtering.
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| 7. |
- Behjat, Hamid, et al.
(författare)
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Spectral Characterization of Functional MRI Data on Voxel-Resolution Cortical Graphs
- 2020
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Ingår i: ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging. - 1945-7928 .- 1945-8452. - 9781538693308 ; 2020-April, s. 558-562
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Konferensbidrag (refereegranskat)abstract
- The human cortical layer exhibits a convoluted morphology that is unique to each individual. Conventional volumetric fMRI processing schemes take for granted the rich information provided by the underlying anatomy. We present a method to study fMRI data on subject-specific cerebral hemisphere cortex (CHC) graphs, which encode the cortical morphology at the resolution of voxels in 3-D. Using graph signal processing principles, we study spectral energy metrics associated to fMRI data, on 100 subjects from the Human Connectome Project database, across seven tasks. Experimental results signify the strength of CHC graphs' Laplacian eigenvector bases in capturing subtle spatial patterns specific to different functional loads as well as to sets of experimental conditions within each task.
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