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97
datasets available to search
ShareScore release 0.9.0
Dataset results
97 results for “Medical Imaging”
QOCA®-Image Medical Platform - Smart VCF Risk Management System
ClinicalTrials.gov study NCT04384211. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Portable Rapid Imaging for Medical Emergencies
ClinicalTrials.gov study NCT06930534. IPD Sharing: NO. Countries: 1. Publications: 0.
Experiment on the Use of Innovative Computer Vision Technologies for Analysis of Medical Images in the Moscow Healthcare System
ClinicalTrials.gov study NCT04489992. IPD Sharing: NO. Countries: 1. Publications: 0.
Three Dimensional Imaging and Wireless Technologies to Enhance Medical Care in Space
ClinicalTrials.gov study NCT00598767. IPD Sharing: NO. Countries: 1. Publications: 0.
Interest of Medical Imaging in the Diagnostic Strategy Vis a Vis a Suspected Horton Disease
ClinicalTrials.gov study NCT02473029. IPD Sharing: Not stated. Countries: 1. Publications: 0.
AI-based Prediction Model of Difficult Tracheal Intubation Using Medical Image Parameters
ClinicalTrials.gov study NCT06982144. IPD Sharing: NO. Countries: 1. Publications: 0.
Acolyte CTO-PCI Study: Imaging and Catheter System to Treat Patients With Coronary Chronic Total Occlusion, Who Have Persistent Symptoms Following Medical Therapy and Are Undergoing Percutaneous Coron
ClinicalTrials.gov study NCT06795763. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of Clinical Efficacy of Augmented Reality (AR)-Based Breast Cancer Medical Imaging Solution (SKIA-Breast) Localization Method in Breast Cancer Patients
ClinicalTrials.gov study NCT07003841. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of the Quality of Life of Patients Requiring Intestinal Cleansing Using Oral Medications to Imaging Procedure by Patient Reported Outcome
ClinicalTrials.gov study NCT02536729. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Comparative Study on Medical Artificial Intelligence Algorithm Assisted and Conventional Imaging Examination Methods
ClinicalTrials.gov study NCT07040527. IPD Sharing: NO. Countries: 0. Publications: 0.
Implementation and Evaluation of Improved Access to Medical Imaging for Geriatric Patients of The Royal Ottawa Hospital
ClinicalTrials.gov study NCT05428475. IPD Sharing: NO. Countries: 0. Publications: 0.
Study of 111In-DAC as an Medical Imaging Agent for Lung Cancer and Brain Cancer Consistent With Metastatic Lung Cancer
ClinicalTrials.gov study NCT00040560. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Precision Medical Diagnosis for Parkinson's Disease - The Quantitative Analysis System for PET/MRI Images in Patients
ClinicalTrials.gov study NCT03909828. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Study of 111In-DAC as a Medical Imaging Agent for the Detection of Breast Cancer
ClinicalTrials.gov study NCT00040430. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Image and Dosimetry Related Research of Department of Medical Physics, Cross Cancer Institute
ClinicalTrials.gov study NCT00314470. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation
<p>Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. The paper can be found at https://arxiv.org/pdf/2206.08023.pdf</p> <p>In addition to providing the labeled 600 CT and MRI scans, we expect to provide 2000 CT and 1200 MRI scans without labels to support more learning tasks (semi-supervised, un-supervised, domain adaption, ...). The link can be found in:</p> <ul> <li><a href="https://zenodo.org/deposit/7262581">labeled data (500CT+100MRI)</a></li> <li><a href="https://zenodo.org/record/7262757#.Y2iSQ9JBwYs">unlabeled data Part I (900CT)</a></li> <li><a href="https://zenodo.org/record/7295661#.Y2iR_9JBwYs">unlabeled data Part II (1100CT)</a> (Now there are 1000CT, we will replenish to 1100CT)</li> <li><a href="https://zenodo.org/record/7295816">unlabeled data Part III (1200MRI)</a></li> </ul> <p>if you found this dataset useful for your research, please cite:</p> <blockquote> <pre>@inproceedings{NEURIPS2022_ee604e1b, author = {Ji, Yuanfeng and Bai, Haotian and GE, Chongjian and Yang, Jie and Zhu, Ye and Zhang, Ruimao and Li, Zhen and Zhanng, Lingyan and Ma, Wanling and Wan, Xiang and Luo, Ping}, booktitle = {Advances in Neural Information Processing Systems}, editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh}, pages = {36722--36732}, publisher = {Curran Associates, Inc.}, title = {AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation}, url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/ee604e1bedbd069d9fc9328b7b9584be-Paper-Datasets_and_Benchmarks.pdf}, volume = {35}, year = {2022} } </pre> </blockquote> <p> </p>
Data for publication: Weighted manifold alignment using wave kernel signatures for aligning medical image datasets
<p>2D dynamic sagittal MR images, corresponding to volunteers E-H in the publication "Weighted manifold alignment using wave kernel signatures for aligning medical image datasets".</p>
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.