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48
datasets available to search
ShareScore release 0.9.0
Dataset results
48 results for “contrastive learning”
SMMGCL: A novel multi-scale graph contrastive learning framework for integrating spatial multi-omics data
Open the record for dataset details and reuse information.
Focal Dual Contrastive Learning for an imbalanced Chinese anesthesia dataset
<p>This is a Chinese anesthesia dataset specifically for anesthesia risk prediction and ASA grading, which contains more than 10,000 real data. If you need it, you can apply for it and give us the direction and reason for your use. We will provide it to you free of charge under reasonable circumstances.</p>
Feasibility of Gadolinium Contrast Reduced Brain MRI: the Potential of Deep Learning
ClinicalTrials.gov study NCT06462924. IPD Sharing: NO. Countries: 0. Publications: 0.
Learning Curve for Minimally Invasive Oesophagectomy and Contrast With Open Procedure
ClinicalTrials.gov study NCT04206696. IPD Sharing: Not stated. Countries: 0. Publications: 0.
AI-Based Self-Supervised Learning Model Using Non-Contrast Breast MRI for Early Screening and Clinical Utility Evaluation
ClinicalTrials.gov study NCT07205276. IPD Sharing: YES. Countries: 0. Publications: 0.
Study on the Performance of a Machine Learning Algorithm Recognizing and Triaging Large Vessel Occlusions Using Non-contrast CT Scans
ClinicalTrials.gov study NCT06216457. IPD Sharing: NO. Countries: 0. Publications: 0.
Dataset: Understanding and Detecting Hateful Content using Contrastive Learning
<p> </p> <p>This is the dataset released with the <a href="https://arxiv.org/abs/2201.08387">paper</a> titled: "<strong>Understanding and Detecting Hateful Content using Contrastive Learning</strong>".</p> <p>We release our dataset in four CSV files. These files contain the textual and visual dataset and the textual and visual ground truth we obtained after our manual annotations. For a detailed description of every <strong><em>column </em></strong>in the CSV structure, along with the type of the <strong><em>value</em></strong>, please read the readme.pdf file provided with this dataset.</p> <p>The images are stored in the zip files. </p> <p>If you find our dataset useful, please cite our paper:</p> <pre><code>@inproceedings{gonzalez2023understanding, title={Understanding and Detecting Hateful Content using Contrastive Learning}, author={Gonz{\'a}lez-Pizarro, Felipe and Zannettou, Savvas}, booktitle={17th International AAAI Conference On Web And Social Media (ICWSM), 2023}, year={2023} } </code></pre> <p>In case of questions, please do not hesitate to contact us: felipegp[at]cs.ubc.ca (<a href="https://gonzalezf.github.io">https://gonzalezf.github.io</a>)</p>
Dataset related to the article "A deep-learning approach for myocardial fibrosis detection in early contrast-enhanced cardiac CT images"
<p>This record contains raw data related to the article "A deep-learning approach for myocardial fibrosis detection in early contrast-enhanced cardiac CT images"</p> <p><strong>Aims:</strong> Diagnosis of myocardial fibrosis is commonly performed with late gadolinium contrast-enhanced (CE) cardiac magnetic resonance (CMR), which might be contraindicated or unavailable. Coronary computed tomography (CCT) is emerging as an alternative to CMR. We sought to evaluate whether a deep learning (DL) model could allow identification of myocardial fibrosis from routine early CE-CCT images.</p> <p><strong>Methods and results:</strong> Fifty consecutive patients with known left ventricular (LV) dysfunction (LVD) underwent both CE-CMR and (early and late) CE-CCT. According to the CE-CMR patterns, patients were classified as ischemic (<em>n</em> =&thinsp;15, 30%) or non-ischemic (<em>n</em> =&thinsp;35, 70%) LVD. Delayed enhancement regions were manually traced on late CE-CCT using CE-CMR as reference. On early CE-CCT images, the myocardial sectors were extracted according to AHA 16-segment model and labeled as with scar or not, based on the late CE-CCT manual tracing. A DL model was developed to classify each segment. A total of 44,187 LV segments were analyzed, resulting in accuracy of 71% and area under the ROC curve of 76% (95% CI: 72%−81%), while, with the bull’s eye segmental comparison of CE-CMR and respective early CE-CCT findings, an 89% agreement was achieved.</p> <p><strong>Conclusions:</strong> DL on early CE-CCT acquisition may allow detection of LV sectors affected with myocardial fibrosis, thus without additional contrast-agent administration or radiational dose. Such tool might reduce the user interaction and visual inspection with benefit in both efforts and time.</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.