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59
datasets available to search
ShareScore release 0.9.0
Dataset results
59 results for “deep-learning”
DeepRNA-Reg: A Deep-Learning Based Approach for Comparative Analysis of CLIP Experiments
GEO Series GSE273503. Mus musculus. 4 samples. Type: Other.
Ultra-fast deep-learned pediatric CNS tumor classification
GEO Series GSE237874. Homo sapiens. 5 samples. Type: Methylation profiling by high throughput sequencing; Other.
Deep-Learned Broadband Encoding Stochastic Filters for Computational Spectroscopic Instruments
<p>Abstract</p> <p>Computational spectroscopic instruments with broadband encoding stochastic (BEST) filters allow the reconstruction of the spectrum at high precision with only a few filters. However, conventional design manners of BEST filters are often heuristic and may fail to fully explore the encoding potential of BEST filters. The parameter constrained spectral encoder and decoder (PCSED)—a neural network-based framework—is presented for the design of BEST filters in spectroscopic instruments. By incorporating the target spectral response definition and the optical design procedures comprehensively, PCSED links the mathematical optimum and practical limits confined by available fabrication techniques. Benefiting from this, a BEST-filter-based spectral camera presents a higher reconstruction accuracy with up to 30 times enhancement and better tolerance to fabrication errors. The generalizability of PCSED is validated in designing metasurface- and interference-thin-film-based BEST filters.</p> <p> </p> <p>Please refer to https://github.com/Hao-Laboratory/PCSED for the source code for data analysis and visualization.</p>
The code and data for "Deep-Learning Correction Methods for WRF Model Precipitation Forecasting from 2014 Through 2022 in Zhengzhou city, China"
<p>The code and data for “Deep-Learning Correction Methods for WRF Model Precipitation Forecasting from 2014 Through 2022 in Zhengzhou city, China”</p>
Supplementary Video 1 of: Ultra-fast deep-learned CNS tumor classification during surgery
<p><strong>Supplementary Video 1:</strong> An intraoperative sequencing experiment (INTRA_4) was captured on film. Audio has been redacted for privacy reasons. The timeline of this specific experiment is shown as a representative example in Fig. 4.</p>
Enhanced Outcome Prediction in Cutaneous Squamous Cell Carcinoma Using Deep-learning and Computational Histopathology
ClinicalTrials.gov study NCT06327971. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Assessment of Liver Diseases Using a Deep-Learning Approach Based on Ultrasound RF-Data
ClinicalTrials.gov study NCT06317181. IPD Sharing: YES. Countries: 1. Publications: 0.
Deep-Learning for Automatic Polyp Detection During Colonoscopy
ClinicalTrials.gov study NCT03637712. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Application of Deep-learning and Ultrasound Elastography in Opportunistic Screening of Breast Cancer
ClinicalTrials.gov study NCT03851497. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Accuracy of Deep-learning Algorithm for Detection and Risk Stratification of Lung Nodules
ClinicalTrials.gov study NCT04022512. IPD Sharing: NO. Countries: 1. Publications: 0.
A New Deep-learning Based Artificial Intelligence Iterative Reconstruction (AIIR) Algorithm in Low-dose Liver CT
ClinicalTrials.gov study NCT05550012. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Deep-learning Based Classification of Spine CT
ClinicalTrials.gov study NCT03790930. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Evaluating Replacement of Standard-of-care Low Dose Computer Tomography (CT) Scans With Radiation-free Bone Imaging by Deep-learning Augmented Zero Echo Time (DL-ZTE) Magnetic Resonance Tomography (MR
ClinicalTrials.gov study NCT06579547. IPD Sharing: NO. Countries: 1. Publications: 0.
Detection of Urinary Stones on ULDCT With Deep-learning Image Reconstruction Algorithm
ClinicalTrials.gov study NCT04490343. IPD Sharing: NO. Countries: 1. Publications: 0.
Gene Regulation Dynamics during the Cell Cycle uncovered by RNA velocity and deep-learning (mESCs)
GEO Series GSE167608. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
Gene Regulation Dynamics during the Cell Cycle uncovered by RNA velocity and deep-learning (IMR90)
GEO Series GSE167607. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Diagnosis of Alzheimer's disease specific phospholipase c gamma-1 SNV by deep-learning based approach for high throughput screening
GEO Series GSE151270. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Bioinformation and deep-learning based model reveals norepinephrine inhibiting PRDX1 aggravates atherosclerosis
GEO Series GSE282003. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing.
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.