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166 results for “Image Classification”
Histological images for MSI vs. MSS classification in gastrointestinal cancer, snap-frozen samples
<p>This repository contains 218,578 unique image patches derived from histological images of colorectal cancer patients in the TCGA cohort (original whole slide SVS images are freely available at https://portal.gdc.cancer.gov/). All images in this repository are derived from snap-frozen tissue slides ("TS" or "BS" at the GDC data portal).</p> <p><strong>Preprocessing</strong></p> <p>All SVS slides were preprocessed as follows</p> <p>1. automatic detection of tumor</p> <p>2. resizing to 224 px x 224 px at a resolution of 0.5 µm/px</p> <p>4. color normalization with the Macenko method (Macenko et al., 2009, http://wwwx.cs.unc.edu/~mn/sites/default/files/macenko2009.pdf)</p> <p>5. assignment of patients to either "MSS" (microsatellite stable) or "MSIMUT" (microsatellite unstable or hypermutated)</p> <p>6. randomization of patients to training and testing sets (~70% and ~30%). Randomization was done on a patient level rather than on a slide or tile level</p> <p>7. equilibration of training sets by undersampling (removing excess tiles in MSS class in a random way)</p> <p><strong>File description</strong></p> <p>1. STAD_TRAIN_MSS - training images (~70% of all patients) for gastric (stomach) cancer TCGA patients with MSS (microsatellite stable) tumors, 50285 unique image patches; FFPE samples</p> <p>2. STAD_TRAIN_MSIMUT - training images ( (~70% of all patients) for gastric (stomach) cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 50285 unique image patches; FFPE samples</p> <p>3. STAD_TEST_MSS - test images (~30% of all patients) for gastric (stomach) cancer TCGA patients with MSS (microsatellite stable) tumors, 90104 unique image patches; FFPE samples</p> <p>4. STAD_TEST_MSIMUT - test images ( ~30% of all patients) for gastric (stomach) cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 27904 unique image patches; FFPE samples</p>
Image data used for publication "Species-level image classification with convolutional neural network enable insect identification from habitus images "
<p>Image-crops of specimens from insect drawers</p> <p>In 2017 we scanned 208 insect drawers containing the collection of british carabids from the Natural History Museum London and extracted crops from the scanned images. This database contain 63.364 specimens that we used to train, validate and test a convolutional neural network.</p> <p>Each folder is named as the gbif id number. E.g. Carabus problematicus is 4470555: https://www.gbif.org/species/4470555</p>
Astronomical Image Classification Dataset
<p>Dataset for the work published in SPIE Sensors and Imaging 2023:</p> <p>Keenan A. A. Chatar, <a href="https://www.spiedigitallibrary.org/profile/ezrafielding">Ezra Fielding</a>, <a href="https://www.spiedigitallibrary.org/profile/Kei.Sano-4227702">Kei Sano</a>, and <a href="https://www.spiedigitallibrary.org/profile/Kentaro.Kitamura-4492438">Kentaro Kitamura</a> "Data downlink prioritization using image classification on-board a 6U CubeSat", Proc. SPIE 12729, Sensors, Systems, and Next-Generation Satellites XXVII, 127290K (19 October 2023); <a href="https://doi.org/10.1117/12.2684047" target="_blank" rel="noopener">https://doi.org/10.1117/12.2684047</a></p>
Digital breast tomosynthesis and contrast-enhanced dual-energy digital mammography alone and in combination compared to 2D digital synthetized mammography and MR imaging in breast cancer detection and classification
<p>We uploaded the dataset of included patients of manuscript: Petrillo A, Fusco R, Vallone P, Filice S, Granata V, Petrosino T, Rosaria Rubulotta M, Setola SV, Mattace Raso M, Maio F, Raiano C, Siani C, Di Bonito M, Botti G. Digital breast tomosynthesis and contrast-enhanced dual-energy digital mammography alone and in combination compared to 2D digital synthetized mammography and MR imaging in breast cancer detection and classification. Breast J. 2020 May;26(5):860-872. doi: 10.1111/tbj.13739. Epub 2019 Dec 30. PMID: 31886607.</p>
Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions
<p>We uploaded the 15 morphological features of 91 samples of 85 patients analyzed in the manuscript: Fusco, Roberta, Adele Piccirillo, Mario Sansone, Vincenza Granata, Paolo Vallone, Maria L. Barretta, Teresa Petrosino, Claudio Siani, Raimondo Di Giacomo, Maurizio Di Bonito, Gerardo Botti, and Antonella Petrillo. 2021. "Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions" Applied Sciences 11, no. 4: 1880. https://doi.org/10.3390/app11041880</p>
The dataset of Sentinel-1 SAR images for sea ice classification
<p>The dataset implementation of the paper "A Multi-scale Dual Attention Network for Automatic Polar Sea Ice Classification Based on Sentinel-1 SAR Images".</p> <p>There are 7381 images as the training set, 1210 images as the validation set, and 3630 images as the test set. </p> <p>The file contains original images and processed images.</p>
Images of H9 cell colonies and code for phenotype classification
<p>The dataset and code are part of the following manuscript submitted for publication in International Journal of Molecular Sciences (MDPI):</p> <p>"Quality Control of Human Pluripotent Stem Cell Colonies by Computational Image Analysis using Convolutional Neural Networks" by Anastasiya Mamaeva, Olga Krasnova, Konstantin Kozlov, Vitaly Gursky, Maria Samsonova and Irina Neganova</p>
Multi-channel auto-encoders for learning domain invariant representations enabling superior classification of histopathology images
<p>A partially synthetic histopathology dataset containing image patches of colon tissue from 3 staining and scanning conditions.</p> <p>This dataset can be used to develop novel histopathology image analysis algorithms that are better able to generalise to novel data domains.</p> <p>See repo for more information.</p>
PASTA-ice sea ice image classification: calibration files and training data
<p>PASTA-ice is a Python-based classification algorithm for aerial sea-ice images. The data set contains calibration files for the CANON EOS-1D Mark III cameras that were implemented in helicopters and POLAR aircraft of the Alfred-Wegener-Institute. Furthermore, it contains training data of labeled sea ice surfaces observed during RV Polarstern cruise PS106 that can be used to train the implemented random forest classifier. Files with extension "sediments" were extended with exemplary data of sediment-loaden snow at the MOSAiC expedition. </p> <p>The PASTA-ice algorithm is available under: <a href="https://github.com/nielsfuchs/pasta_ice">https://github.com/nielsfuchs/pasta_ice</a></p>
Canine mammary tumors histopathological image classification by computer-aided pathology_ supplementary files
<p>Supplementary files</p>
Deep Ensemble Learning and Transfer Learning Methods for Classification of Senescent Cells from Nonlinear Optical Microscopy Images
<p>This Dataset contains the train and test NLO images in pickle format used for the following publication: Deep Ensemble Learning and Transfer Learning Methods for Classification of Senescent Cells from Nonlinear Optical Microscopy Images</p>
image classification dataset on tailored textiles quality control
<p>This dataset was geared towards representing practical quality control scenarios, specifically involving the quality inspection of glass fiber fabric. Continuous rolls of glass fiber fabric were cut into samples of 300x200 mm. Half of these samples were reinforced with a single carbon fiber. These samples were then classified into six different categories based on the presence of common defects or if they were error-free textiles. Each category consists of 300 images, with a resolution of 4288x2848 pixels.</p>
Datasets for a data-centric image classification benchmark for noisy and ambiguous label estimation
<p>This is the official data repository of the Data-Centric Image Classification (DCIC) Benchmark. The goal of this benchmark is to measure the impact of tuning the dataset instead of the model for a variety of image classification datasets. Full details about the collection process, the structure and automatic download at</p> <p>Paper: https://arxiv.org/abs/2207.06214</p> <p>Source Code: https://github.com/Emprime/dcic</p> <p>The license information is given below as download.</p> <p><strong>Citation</strong></p> <p>Please cite as</p> <pre><code>@article{schmarje2022benchmark, author = {Schmarje, Lars and Grossmann, Vasco and Zelenka, Claudius and Dippel, Sabine and Kiko, Rainer and Oszust, Mariusz and Pastell, Matti and Stracke, Jenny and Valros, Anna and Volkmann, Nina and Koch, Reinahrd}, journal = {36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks}, title = {{Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation}}, year = {2022} }</code></pre> <p>Please see the full details about the used datasets below, which should also be cited as part of the license.</p> <pre><code>@article{schoening2020Megafauna, author = {Schoening, T and Purser, A and Langenk{\"{a}}mper, D and Suck, I and Taylor, J and Cuvelier, D and Lins, L and Simon-Lled{\'{o}}, E and Marcon, Y and Jones, D O B and Nattkemper, T and K{\"{o}}ser, K and Zurowietz, M and Greinert, J and Gomes-Pereira, J}, doi = {10.5194/bg-17-3115-2020}, journal = {Biogeosciences}, number = {12}, pages = {3115--3133}, title = {{Megafauna community assessment of polymetallic-nodule fields with cameras: platform and methodology comparison}}, volume = {17}, year = {2020} } @article{Langenkamper2020GearStudy, author = {Langenk{\"{a}}mper, Daniel and van Kevelaer, Robin and Purser, Autun and Nattkemper, Tim W}, doi = {10.3389/fmars.2020.00506}, issn = {2296-7745}, journal = {Frontiers in Marine Science}, title = {{Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification}}, volume = {7}, year = {2020} } @article{peterson2019cifar10h, author = {Peterson, Joshua and Battleday, Ruairidh and Griffiths, Thomas and Russakovsky, Olga}, doi = {10.1109/ICCV.2019.00971}, issn = {15505499}, journal = {Proceedings of the IEEE International Conference on Computer Vision}, pages = {9616--9625}, title = {{Human uncertainty makes classification more robust}}, volume = {2019-Octob}, year = {2019} } @article{schmarje2019, author = {Schmarje, Lars and Zelenka, Claudius and Geisen, Ulf and Gl{\"{u}}er, Claus-C. and Koch, Reinhard}, doi = {10.1007/978-3-030-33676-9_26}, issn = {23318422}, journal = {DAGM German Conference of Pattern Regocnition}, number = {November}, pages = {374--386}, publisher = {Springer}, title = {{2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy}}, volume = {11824 LNCS}, year = {2019} } @article{schmarje2021foc, author = {Schmarje, Lars and Br{\"{u}}nger, Johannes and Santarossa, Monty and Schr{\"{o}}der, Simon-Martin and Kiko, Rainer and Koch, Reinhard}, doi = {10.3390/s21196661}, issn = {1424-8220}, journal = {Sensors}, number = {19}, pages = {6661}, title = {{Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy}}, volume = {21}, year = {2021} } @article{schmarje2022dc3, author = {Schmarje, Lars and Santarossa, Monty and Schr{\"{o}}der, Simon-Martin and Zelenka, Claudius and Kiko, Rainer and Stracke, Jenny and Volkmann, Nina and Koch, Reinhard}, journal = {Proceedings of the European Conference on Computer Vision (ECCV)}, title = {{A data-centric approach for improving ambiguous labels with combined semi-supervised classification and clustering}}, year = {2022} } @article{obuchowicz2020qualityMRI, author = {Obuchowicz, Rafal and Oszust, Mariusz and Piorkowski, Adam}, doi = {10.1186/s12880-020-00505-z}, issn = {1471-2342}, journal = {BMC Medical Imaging}, number = {1}, pages = {109}, title = {{Interobserver variability in quality assessment of magnetic resonance images}}, volume = {20}, year = {2020} } @article{stepien2021cnnQuality, author = {St{\c{e}}pie{\'{n}}, Igor and Obuchowicz, Rafa{\l} and Pi{\'{o}}rkowski, Adam and Oszust, Mariusz}, doi = {10.3390/s21041043}, issn = {1424-8220}, journal = {Sensors}, number = {4}, title = {{Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment}}, volume = {21}, year = {2021} } @article{volkmann2021turkeys, author = {Volkmann, Nina and Br{\"{u}}nger, Johannes and Stracke, Jenny and Zelenka, Claudius and Koch, Reinhard and Kemper, Nicole and Spindler, Birgit}, doi = {10.3390/ani11092655}, journal = {Animals 2021}, pages = {1--13}, title = {{Learn to train: Improving training data for a neural network to detect pecking injuries in turkeys}}, volume = {11}, year = {2021} } @article{volkmann2022keypoint, author = {Volkmann, Nina and Zelenka, Claudius and Devaraju, Archana Malavalli and Br{\"{u}}nger, Johannes and Stracke, Jenny and Spindler, Birgit and Kemper, Nicole and Koch, Reinhard}, doi = {10.3390/s22145188}, issn = {1424-8220}, journal = {Sensors}, number = {14}, pages = {5188}, title = {{Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks}}, volume = {22}, year = {2022} }</code></pre> <p>Addition: This repository also contains the original data from the paper "Annotating Ambiguous Images" (https://arxiv.org/abs/2306.12189). The data is created based on the original datasets and license from https://osf.io/t98fz/ and https://osf.io/nqjyw/</p>
Non-contrast Enhanced Cardiac Magnetic Resonance Imaging in the Diagnosis and Classification of Pulmonary Hypertension
ClinicalTrials.gov study NCT01725763. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Implications for Management of PET Amyloid Classification Technology in the Imaging Dementia(IDEAS) Trial
ClinicalTrials.gov study NCT02781220. IPD Sharing: UNDECIDED. Countries: 1. Publications: 50.
Imaging-Guided Classification for Endophytic Renal Tumors: PN Strategies & Outcomes
ClinicalTrials.gov study NCT06954571. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Diabetic Retinopathy Classification: ETDRS 7-fields vs Widefield Imaging (ClarusDR)
ClinicalTrials.gov study NCT05746975. IPD Sharing: Not stated. Countries: 1. Publications: 15.
Computer-based Classification of Colorectal Polyps Using Narrow-band Imaging
ClinicalTrials.gov study NCT01262248. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Image-based taxonomic classification of bulk biodiversity samples using deep learning and domain adaptation
Open the record for dataset details and reuse information.
AVHRR/Landsat TM Classifed images at 1km and 25km grain size centered on Bonanza Creek LTER
AVHRR/Landsat TM Classifed images at 1km and 25km grain size centered on Bonanza Creek LTER
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.