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14 results for “computational pathology”
FIGURE 7 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone
FIGURE 7. Location and geologic position of the Woodbine Group. A. General stratigraphic sequence and timescale for the Cretaceous of central and north central Texas showing the position of the Woodbine Group. Position of the AAS within the Woodbine is marked with an arrowhead. Terrestrial deposits represented by stippled intervals. Time scale based on Denne et al. (2016). Modified from Adams et al. (2011). B. Generalized map of geological units present as surface exposures in the Fort Worth basin with location of AAS shown. Modified after Strganac (2015) and Barnes et al. (1972).
FIGURE 4 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone
FIGURE 4. Modern Trachemys scripta plastron elements (UTK 2317) with shell disease. Photograph (A) and orthographic model based on µCT data (B) shown in ventral view. Frames on the photograph and model highlight specific areas of shell disease, shown on the right as both direct µCT data (C, E, G) and heatmapped slices illustrating bone density changes (D, F, H). In the heatmapped cross sections, colors range from purple (lowest density), to orange (medium density), to white (highest density). Patches of shell disease are indicated with purple arrows. Scale bars in A and B equal 5 cm. Scale bars in C, E, and G equal 5 mm.
FIGURE 2 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone
FIGURE 2. Fossil turtle shell fragments (DMNH 2013-07-1319) with putative bite marks. Photographs (A, G) and orthographic models based on µCT data (B, H) shown in external view. Frames on the photograph and model highlight specific areas with bite marks as both direct µCT data (C, E, I) and heatmapped slices illustrating bone density changes (D, F, J). In the heatmapped cross sections, colors range from purple (lowest density), to orange (medium density), to white (highest density). Specific bite marks are indicated with purple arrows. Scale bars in A, B, G, and H equal 2 cm. Scale bars in C, E, and I equal 5 mm.
FIGURE 1 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone
FIGURE 1. Modern Trachemys scripta shell (SAAF) with bite marks attributed to Mecistops cataphractus. Orthographic models of the shell, based on µCT data shown in dorsal (A) and ventral (B) views. Frames on the models highlight specific bite marks, shown on the right as both direct µCT data (C, E, G) and heatmapped slices illustrating bone density changes (D, F, H). In the heatmapped cross sections, colors range from purple (lowest density), to orange (medium density), to white (highest density). Specific bite marks are indicated with purple arrows. Scale bars in A and B equal 5 cm. Scale bars in C, E, and G equal 5 mm.
FIGURE 6 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone
FIGURE 6. Fossil turtle shell fragment (DMNH 2013-07-0563) with putative shell disease.. Photograph (A) shown in external view. Frames on the photograph and highlight specific areas with shell disease as both direct µCT data (B, D) and heatmapped slices illustrating bone density changes (C, E). In the heatmapped cross sections, colors range from purple (lowest density), to orange (medium density), to white (highest density). Specific patches of shell disease are indicated with purple arrows. Scale bar in A equals 2 cm. Scale bars in B and D equal 5 mm.
FIGURE 8 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone
FIGURE 8. Characteristic examples of shell disease and bite marks in modern and fossil turtle shells. Modern shell disease on the plastron of Trachemys scripta, specimen UTK 2317 (A). Modern bite marks (bisected punctures) on the plastron of Trachemys scripta, specimen SAAF unnumbered (B). Fossil shell disease on a fragment of turtle shell, specimen DMNH 2013-07-0563 (C). Fossil bite marks (four scores and one pit) on a fragment of turtle shell, specimen DMNH 2013-07-1319 (D). Scale bars equal 10 mm.
FIGURE 3 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone
FIGURE 3. Fossil turtle shell fragment (DMNH 2013-07-0567) with putative bite marks. Photograph (A) and orthographic model based on µCT data (B) shown in external view. Frames on the photograph and model highlight specific areas with bite marks as both direct µCT data (C, E) and heatmapped slices illustrating bone density changes (D, F). In the heatmapped cross sections, colors range from purple (lowest density), to orange (medium density), to white (highest density). Specific bite marks are indicated with purple arrows. Scale bars in A and B equal 2 cm. Scale bars in C and E equal 5 mm.
FIGURE 5 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone
FIGURE 5. Modern Trachemys scripta plastron and partial carapace elements (UTK 1844) with shell disease. Photograph (A) and orthographic model based on µCT data (B) shown in ventral view. Frames on the photograph and model highlight specific areas of shell disease, shown on the right as both direct µCT data (C, E, G) and heatmapped slices illustrating bone density changes (D, F, H). In the heatmapped cross sections, colors range from purple (lowest density), to orange (medium density), to white (highest density). Patches of shell disease are indicated with purple arrows. Scale bars in A and B equal 5 cm. Scale bars in C, E, and G equal 5 mm.
On Transferability of Histological Tissue Labels in Computational Pathology
<p>This Zip file contains all Convolutional Neural Network (CNN) models of HistoNet trained on ADP database introduced in</p> <p><em>Hosseini et. al. “On Transferability of Histological Tissue Labels in Computational Pathology.” Accepted in the European Conference on Computer Vision (ECCV), Glasgow, UK, August 2020 </em></p> <p>For more information on the models and how to train them, please refer to <strong>https://github.com/mahdihosseini/HistoLabelTransfer</strong></p> <p> </p>
Profiling of pancreatic adenocarcinoma using artificial intelligence-based integration of multi-omic and computational pathology features - Validation Data Sets
<p>Two public validation cohorts were utilized in the MT-Pilot study, the Cancer Genome Atlas (TCGA) and cohort-1 Johns Hopkins University (JHU). These datasets included DNA, RNA, clinical data, and tissue protein analytes analyzed for survival outcome prediction using AI/Machine Learning modeling. </p>
Kaisa Helminen is CEO of Fimmic Oy, a Finnish company that created the first commercial tool integrating deep learning and computer vision for pathology research. Photograph: Sebastian Mardones / Health Capital Helsinki. in Deep learning brings speed, accuracy to the life sciences.
Kaisa Helminen is CEO of Fimmic Oy, a Finnish company that created the first commercial tool integrating deep learning and computer vision for pathology research. Photograph: Sebastian Mardones / Health Capital Helsinki.
Canine mammary tumors histopathological image classification by computer-aided pathology_ supplementary files
<p>Supplementary files</p>
Data from: Computational pathology to discriminate benign from malignant intraductal proliferations of the breast
Open the record for dataset details and reuse information.
Comparing Standard of Care Biopsy System to a Novel Biopsy Needle System by Computational Pathologic Analysis
ClinicalTrials.gov study NCT06830265. IPD Sharing: NO. Countries: 0. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.