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129
datasets available to search
ShareScore release 0.9.0
Dataset results
129 results for “Deep neural network”
AtacWorks: A deep convolutional neural network toolkit for epigenomics
GEO Series GSE147113. Homo sapiens. 3 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Assessing the Generalizability of Deep Neural Networks-Based Models for Black Skin Lesions
<p>Melanoma is the most severe type of skin cancer due to its ability to cause metastasis. It is more common in black people, often affecting acral regions: palms, soles, and nails. Deep neural networks have shown tremendous potential for improving clinical care and skin cancer diagnosis. Nevertheless, prevailing studies predominantly rely on datasets of white skin tones, neglecting to report diagnostic outcomes for diverse patient skin tones. In this work, we evaluate supervised and self-supervised models in skin lesion images extracted from acral regions commonly observed in black individuals. Also, we carefully curate<strong> a dataset containing skin lesions in acral regions and assess the datasets concerning the Fitzpatrick scale to verify performance on black skin.</strong> Our results expose the poor generalizability of these models, revealing their favorable performance for lesions on white skin. Neglecting to create diverse datasets, which necessitates the development of specialized models, is unacceptable. Deep neural networks have great potential to improve diagnosis, particularly for populations with limited access to dermatology. However, including black skin lesions is necessary to ensure these populations can access the benefits of inclusive technology.</p>
NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products
<p>Dataset for NPClassifier project</p>
Predicting code comprehension: a novel approach to align human gaze with code using deep neural networks
<p>Supplementary data and scripts intended for submission review only. </p>
Accurate Diagnosis of the Invasion Depth in ESCC by a Deep Neural Network Analysis of NBI Endoscopy Data
ClinicalTrials.gov study NCT06252974. IPD Sharing: Not stated. Countries: 0. Publications: 0.
A transcriptome-based deep neural network classifier for identifying the site of origin in mucinous cancer
GEO Series GSE163126. Homo sapiens. 14 samples. Type: Expression profiling by high throughput sequencing.
Dataset related to article "Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment"
<p>This record contains raw data related to article “Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment"</p> <p>Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the "most infected volume" composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.</p>
maxATAC: genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks
GEO Series GSE197009. Homo sapiens. 3 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Dataset of "Parts-per-Object Count in Agricultural Images: Solving Phenotyping Problems via a Single Deep Neural Network" paper
<p>This includes the relevant datasets to: </p> <p>Khoroshevsky, F., Khoroshevsky, S., & Bar-Hillel, A. (2021). Parts-per-object count in agricultural images: Solving phenotyping problems via a single deep neural network. Remote Sens. 13(13), 2496.<br> https://doi.org/10.3390/rs13132496<br> </p> <p>Datasets related to wheat and banana are not public since it belongs to the Israel Phenomics consortium.</p> <p>This research was funded by the Generic technological R&D program of the Israel innovation<br> authority-the Phenomics consortium, and the Ministry of Science & Technology, Israel.</p> <p> </p> <p> </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.