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166
datasets available to search
ShareScore release 0.9.0
Dataset results
166 results for “Image Classification”
Improving Histopathological Image Classification with Patch-based Embeddings and Ensemble Learning: A Study on Enteroscope Biopsy Images
<p>This dataset contains the application codes developed for Enteroscope Biopsy Histopathological images and the images divided into patches in the dataset.</p>
A high accuracy of deep learning based CNN architecture: classic, VGGNet, and RestNet50 for Covid-19 image classification
Open the record for dataset details and reuse information.
Radiomics Model Based on DCE-MRI and Ultrasound Images for Breast Lesion Classification
ClinicalTrials.gov study NCT06497023. IPD Sharing: NO. Countries: 1. Publications: 0.
Narrow-band Imaging Diagnostic Classification System for Bladder Tumors
ClinicalTrials.gov study NCT05611762. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Machine Learning to Analyze Facial Imaging, Voice and Spoken Language for the Capture and Classification of Cancer/Tumor Pain
ClinicalTrials.gov study NCT04442425. IPD Sharing: YES. Countries: 1. Publications: 0.
Clinical Classification and Imaging Markers in Patients of Obesity
ClinicalTrials.gov study NCT06649071. IPD Sharing: NO. Countries: 1. Publications: 0.
Staging Classification of Severe Tricuspid Regurgitation Using Novel Cardiac Imaging Techniques
ClinicalTrials.gov study NCT05006443. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Deep Learning for Classification of Scheimpflug Corneal Tomography Images
ClinicalTrials.gov study NCT04497207. IPD Sharing: NO. Countries: 1. Publications: 0.
Pathological Classification of Pulmonary Nodules in Images Using Deep Learning
ClinicalTrials.gov study NCT05221814. IPD Sharing: Not stated. Countries: 1. Publications: 0.
MRI Functional Imaging Characteristics and Fat Quantification of CT-fat-free Renal Neoplasms: Relationships With Histological Classifications and Molecular Markers
ClinicalTrials.gov study NCT06126159. IPD Sharing: NO. Countries: 1. Publications: 0.
Training Physicians to Differentiate the Paris Classification Using Artificial Colon Polyp Images
ClinicalTrials.gov study NCT06550908. IPD Sharing: NO. Countries: 1. Publications: 0.
Diagnostic Performance of an AI-based Model for TCM Constitution Classification Using Ophthalmic Imaging
ClinicalTrials.gov study NCT07127939. IPD Sharing: NO. Countries: 1. Publications: 0.
Development of an Artificial Intelligence-Based Model for Predicting Difficult Intubation Using Video Laryngoscopic Images and Cormack-Lehane Classification
ClinicalTrials.gov study NCT07152093. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Ultrasound Liver Imaging for Classification of Metabolic Dysfunction-associated Steatotic Liver Disease
ClinicalTrials.gov study NCT07373769. IPD Sharing: NO. Countries: 1. Publications: 0.
Photoacoustic/Ultrasound Imaging for Breast Nodule Diagnosis, Molecular Classification, and Neoadjuvant Chemotherapy Response Evaluation
ClinicalTrials.gov study NCT06908291. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Classification of Ankle Injury Observed With X-ray Combined With Magnetic Resonance Imaging
ClinicalTrials.gov study NCT02971943. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
IceBridge-Related DMS-Derived L4 Sea Ice Surface Cover Classification Orthorectified Images V001
This data set contains reprocessed, orthorectified images depicting labels that indicate the sea ice surface category, created by processing <a href="https://nsidc.org/data/iodms0">IceBridge DMS L0 Raw Imagery</a> with the Open Source Sea-ice Processing Algorithm. Orthorectification was done using digital elevation models from the <a href="https://nsidc.org/data/iodem3">IceBridge DMS L3 Ames Stereo Pipeline Photogrammetric DEM</a> collection. The standard (non-orthorectified) images are available as <a href="https://nsidc.org/data/rdsisc4">IceBridge-Related DMS-Derived L4 Sea Ice Surface Cover Classification Images</a>.
Land Cover Classification, Snow Cover, and Fractional Snow-Covered Area Maps from Maxar WorldView Satellite Images V001
This data set includes: (1) fine-scale snow and land cover maps from two mountainous study sites in the Western U.S., produced using machine-learning models trained to extract land cover data from WorldView-2 and WorldView-3 stereo panchromatic and multispectral images; (2) binary snow maps derived from the land cover maps; and (3) 30 m and 465 m fractional snow-covered area (fSCA) maps, produced via downsampling of the binary snow maps. The land cover classification maps feature between three and six classes common to mountainous regions and integral for accurate stereo snow depth mapping: illuminated snow, shaded snow, vegetation, exposed surfaces, surface water, and clouds. Also included are Landsat and MODSCAG fSCA map products. The source imagery for these data are the Maxar WorldView-2 and Maxar WorldView-3 Level-1B 8-band multispectral images, orthorectified and converted to top-of-atmosphere reflectance. These Level-1B images are available under the NGA NextView/EnhancedView license.
IceBridge-Related DMS-Derived L4 Sea Ice Surface Cover Classification Images V001
This data set contains reprocessed images depicting labels that indicate the sea ice surface category, created by processing <a href="https://nsidc.org/data/iodms0">IceBridge DMS L0 Raw Imagery</a> with the Open Source Sea-ice Processing Algorithm. An orthorectified version of this data set is available as <a href="https://nsidc.org/data/rdsisco4">IceBridge-Related DMS-Derived L4 Sea Ice Surface Cover Classification Orthorectified Images</a>.
MULTI-TEMPORAL REMOTE SENSING IMAGE CLASSIFICATION - A MULTI-VIEW APPROACH
MULTI-TEMPORAL REMOTE SENSING IMAGE CLASSIFICATION - A MULTI-VIEW APPROACH VARUN CHANDOLA* AND RANGA RAJU VATSAVAI* Abstract. Multispectral remote sensing images have been widely used for automated land use and land cover classification tasks. Often thematic classification is done using single date image, however in many instances a single date image is not informative enough to distinguish between different land cover types. In this paper we show how one can use multiple images, collected at different times of year (for example, during crop growing season), to learn a better classifier. We propose two approaches, an ensemble of classifiers approach and a co-training based approach, and show how both of these methods outperform a straightforward stacked vector approach often used in multi-temporal image classification. Additionally, the co-training based method addresses the challenge of limited labeled training data in supervised classification, as this classification scheme utilizes a large number of unlabeled samples (which comes for free) in conjunction with a small set of labeled training data.
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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.