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56 results for “Image Recognition”
Figure 1 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187
Figure 1 In the Central Library of Algorithms, natural history collection staff will select algorithms (feature extractors, models, etc.) that are most appropriate for the identification of their target organisms and add them to the workbench. The current figure shows a mock-up.
Figure 6 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187
Figure 6 Mock-up of an interface for automated taxon identification. Naturalis holds over 500.000 specimens of unmounted, unsorted and often unidentified, papered butterflies and moths that were collected mostly in Europe and Asia over the past 200 years. In early 2016, Naturalis embarked on a 10-year-project to digitally identify all these specimens with the help of dedicated volunteers (Caspers et al. 2019). Specimens are unpacked, photographed, had their label data registered and then repacked, still unmounted, for long-term storage. Specimen images were then dragged and dropped into a web-based interface to get a near-instant response with multiple predictions about the taxonomic identity including probability values.
Figure 5 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187
Figure 5 Non-expert collection staff easily find and afterwards sort specimens by taxon (line color) and by accuracy of the identification (line type). The insect drawer is from the Oxford University Museum of Natural History. The current figure shows a mock-up.
Figure 4 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187
Figure 4 Algorithms recognize and number individual specimens in a drawer of unsorted items. The insect drawer is from the Oxford University Museum of Natural History. The current figure shows a mock-up.
IMPORTANCE OF STUDENT IMAGE RECOGNITION PROGRAM IN LEARNING MANAGEMENT.
Open the record for dataset details and reuse information.
Establishment and Evaluation of Multimodal Image Recognition System of Glioma Based on Deep Learning
ClinicalTrials.gov study NCT04407039. IPD Sharing: UNDECIDED. Countries: 0. Publications: 4.
Relevance of aquaporin-magnetic resonance imaging in the early recognition of Alzheimer's disease
<p>The retrospective study includes<strong> </strong>patients with mild cognitive impairment (MCI) (60-85 years old, n=50), AD patients (60-85 years old, n=40) diagnosed in hospitals between January 2020 to January 2023, and age-matched normal control (NC) group of cognitively healthy individuals (n=40). All subjects underwent AQP-MRI examination to assess MRI grades. AQP-MRI voxel values of thalamus, substantia nigra, and pallidum, AQP apparent diffusion coefficients (AQP-ADC) and ADC values of regions of interest were recorded. Spearman's correlation was used to determine the correlation between AQP-ADC, ADC values and MCI, AD, ROC curve was used to judge the diagnostic value of AQP-ADC, AD values for MCI, AD.</p>
Rapid Diagnosis and Prognosis Recognition of Imaging and Biomarkers in Mild to Moderate Traumatic Brain Injury
ClinicalTrials.gov study NCT05108909. IPD Sharing: Not stated. Countries: 1. Publications: 0.
SPY Fluorescence Imaging Systems and Indocyanine Green to Determine the Percentage of Successful Critical Anatomy Recognition in Laparoscopic Cholecystectomy Surgeries.
ClinicalTrials.gov study NCT05006950. IPD Sharing: NO. Countries: 1. Publications: 0.
Endoscopic Severity Image Recognition to Advance Research and Training in Inflammatory Bowel Disease (EVEREST - IBD)
ClinicalTrials.gov study NCT04867408. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
The Use of Magnetic Endoscopic Imagers During Colonoscopy for Loop Recognition and Resolution
ClinicalTrials.gov study NCT02109536. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Information Extraction and Database Construction for Emergent Patients Based on Voice and Image Recognition Technology
ClinicalTrials.gov study NCT04918979. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Research on the Application of AI Image Recognition-Based Smartphone Apps in Personalized Bowel Preparation
ClinicalTrials.gov study NCT06610630. IPD Sharing: NO. Countries: 1. Publications: 0.
Recognition of cellular RNAs by the S9.6 antibody creates pervasive artifacts when imaging RNA:DNA hybrids
GEO Series GSE141833. Homo sapiens. 18 samples. Type: Other.
Scaled and Translated Image Recognition (STIR) Source Data
<p>While convolutions are known to be invariant to (discrete) translations, scaling continues to be a challenge and most image recognition networks are not invariant to them. To explore these effects, we have created the Scaled and Translated Image Recognition (STIR) dataset. This dataset contains objects of size <span class="math-tex">\(s \in [17,64]\)</span>, each randomly placed in a <span class="math-tex">\(64 \times 64\)</span> pixel image.</p> <p><strong>Original Source Data</strong></p> <ul> <li><code>dota/</code> (from <a href="https://captain-whu.github.io/DOTA/dataset.html">DOTA v1.5 Google Drive</a> website) <ul> <li><code>train/</code> <ul> <li><code>DOTA-v1.5_train.zip</code> <strong>not</strong> unzipped</li> <li><code>part1.zip</code> <strong>not</strong> unzipped</li> <li><code>part2.zip</code> <strong>not</strong> unzipped</li> <li><code>part3.zip</code> <strong>not</strong> unzipped</li> </ul> </li> <li><code>val/</code> <ul> <li><code>DOTA-v1.5_val.zip</code> <strong>not</strong> unzipped</li> <li><code>part1.zip</code> <strong>not</strong> unzipped</li> </ul> </li> </ul> </li> <li><code>fontawesome/</code> (from <a href="https://fontawesome.com/v5/download">Font Awesome</a> 5.15.3 "Free for Desktop") <ul> <li><code>svgs/</code> unzipped from archive</li> </ul> </li> <li><code>mapillary/</code> (from <a href="https://www.mapillary.com/dataset/trafficsign">Mapillary Traffic Sign Dataset</a>) <ul> <li><code>mtsd_v2_fully_annotated</code> unzipped from archive</li> <li><code>train.0.zip</code> <strong>not</strong> unzipped</li> <li><code>train.1.zip</code> <strong>not</strong> unzipped</li> <li><code>train.2.zip</code> <strong>not</strong> unzipped</li> <li><code>val.zip</code> <strong>not</strong> unzipped</li> </ul> </li> <li><code>mnist/</code> (from <a href="http://yann.lecun.com/exdb/mnist/">Yann LeCun</a> website) <ul> <li><code>t10k-images-idx3-ubyte.gz</code></li> <li><code>t10k-labels-idx1-ubyte.gz</code></li> <li><code>train-images-idx3-ubyte.gz</code></li> <li><code>train-labels-idx1-ubyte.gz</code></li> </ul> </li> </ul> <p><strong>License and Attribution</strong></p> <p>When using the original source data for your own research, please respect the individual licenses. For attribution in papers, we recommend the following citations which introduce the respective datasets.</p> <ol> <li>D. Gandy, J. Otero, E. Emanuel, F. Botsford, J. Lundien, K. Jackson, M. Wilkerson, R. Madole, J. Raphael, T. Chase, G. Taglialatela, B. Talbot, and T. Chase. Font Awesome. https://fontawesome.com/v5/download, Nov. 2022.</li> <li>Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. <em>Proc. IEEE</em>, 86(11):2278–2324, Nov. 1998.</li> <li> C. Ertler, J. Mislej, T. Ollmann, L. Porzi, G. Neuhold, and Y. Kuang. The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale. In <em>2020 16th Eur. Conf. Comput. Vision (ECCV)</em>, Glasgow, UK, Aug. 2020.</li> <li>G.-S. Xia, X. Bai, J. Ding, Z. Zhu, S. Belongie, J. Luo, M. Datcu, M. Pelillo, and L. Zhang. DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. In <em>2018 IEEE/CVF Conf. Comput. Vision and Pattern Recognition (CVPR)</em>, pages 3974–3983, Salt Lake City, UT, USA, June 2018.</li> </ol>
The raw images of Laser Confocal Microscopy experiments in the manuscript: Inert Pepper aptamer-mediated endogenous mRNA recognition and imaging in living cells
<p>The <strong>original imaging data</strong> folder contains the raw images of Laser confocal microscopy experiments in the manuscript: Inert Pepper aptamer-mediated endogenous mRNA recognition and imaging in living cells. <a href="https://doi.org/10.1093/nar/gkac368">https://doi.org/10.1093/nar/gkac368</a> </p>
ScienceDex guides
Understand access before you commit
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.