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datasets available to search
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Dataset results
173 results for “convolutional neural networks”
A functional genomics atlas enhanced by convolutional neural networks facilitates clinical interpretation of disease relevant variants in non-coding regulatory elements [plasmid DNA-seq]
GEO Series GSE263336. Homo sapiens. 4 samples. Type: Other.
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.
LyNoS: Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical prior guiding
Open the record for dataset details and reuse information.
MetDIT: Transforming and Analyzing Clinical Metabolomics Data with Convolutional Neural Networks
<h1>MetDIT: Transforming and Analyzing Clinical Metabolomics Data with Convolutional Neural Networks</h1>
Towards Fast Region Adaptive Ultrasound Beamformer For Plane Wave Imaging Using Convolutional Neural Networks
<p>This dataset is supplementary to the <a href="https://ieeexplore.ieee.org/document/9630930">IEEE EMBC 2021 paper titled "Towards Fast Region Adaptive Ultrasound Beamformer for Plane Wave Imaging Using Convolutional Neural Networks"</a></p> <p>The dataset is in .mat format and has two variables as below:</p> <p>tofc: Time of flight corrected (delay compensated) input data</p> <p>beamformedData: The delay and sum beamformed (pre-envelope) data</p> <p>The data is in int16 format and may need to be converted to double/float for improved results.</p> <p><strong>Dataset Access: </strong>You need to download the agreement in the <a href="https://drive.google.com/file/d/1tei07_xzcOLTdHpEXUtFcgNCwjxoAynW/view?usp=sharing" target="_blank" rel="noopener">link </a>and submit the form along with the agreement in the <a href="https://forms.gle/RaXtPR12wfrhbotf9" target="_blank" rel="noopener">link</a></p>
Windy events detection in big bioacoustics datasets using a pre-trained Convolutional Neural Network
<p>This repository icludes the code and all relevant files used throughout our study. These encompass everything from the initial sheets of the whole acoustic dataset utilised for selecting the annotated dataset to the notebook (.ipynb) and the recordings employed in training the model.</p>
Data set used in "FACIAL WRINKLE CATEGORIZATION USING CONVOLUTIONAL NEURAL NETWORK"
<p><span>For the purpose of training the neural network, a total of 5,098 images were provided, collected over a period of 3 years. These images were categorized into 4 classes, with the number of images in each category as evenly balanced as possible, with minimal deviation from the ideal distribution</span>. <span>A tool for the detection and classification of wrinkles is provided in this way.</span></p>
A Convolutional Neural Network for Difficult Biliary Cannulation
ClinicalTrials.gov study NCT07389915. IPD Sharing: NO. Countries: 0. Publications: 0.
Convolutional neural network modelling: advancing identification of true mRNA cleavage sites
GEO Series GSE163382. Solanum tuberosum; Phytophthora infestans. 35 samples. Type: Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
Dataset used to train a Convolutional Neural Network dedicated to skin pore detection and classification
<p>This data note pertains to the dataset used during the training of a neural network dedicated to the classification of facial skin based on pore condition. The dataset comprises a total of 3086 images categorized into 5 folders, thereby defining a total of 5 classes for facial skin categorization. The folders are labeled as numbers: 1, 2, 3, 4, and 5, corresponding to the categories of facial skin: very good, good, normal, poor, very poor. All images are in "jpg" format with a resolution of 224x224 pixels. On the this way prepared dataset, without any additional processing, is accepted as input by functions from the Keras library within the TensorFlow development environment.</p>
Dataset related to the article " Automated Left and Right Ventricular Chamber Segmentation in Cardiac Magnetic Resonance Images Using Dense Fully Convolutional Neural Network"
<p>This record contains raw data related to the article " Automated left and right ventricular chamber segmentation in cardiac magnetic resonance images using dense fully convolutional neural network"</p> <p><br> Background and objective: Segmentation of the left ventricular (LV) myocardium (Myo) and RV endocardium on cine cardiac magnetic resonance (CMR) images represents an essential step for cardiacfunction evaluation and diagnosis. In order to have a common reference for comparing segmentation algorithms, several CMR image datasets were made available, but in general they do not include the most apical and basal slices, and/or gold standard tracing is limited to only one of the two ventricles, thus not fully corresponding to real clinical practice. Our aim was to develop a deep learning (DL) approach for automated segmentation of both RV and LV chambers from short-axis (SAX) CMR images, reporting separately the performance for basal slices, together with the applied criterion of choice.<br> Method: A retrospectively selected database (DB1) of 210 cine sequences (3 pathology groups) was considered: images (GE, 1.5 T) were acquired at Centro Cardiologico Monzino (Milan, Italy), and end-diastolic (ED) and end-systolic frames (ES) were manually segmented (gold standard, GS). Automatic ED and ES RV and LV segmentation were performed with a U-Net inspired architecture, where skip connections were redesigned introducing dense blocks to alleviate the semantic gap between the U-Net encoder and decoder. The proposed architecture was trained including: A) the basal slices where the Myo surrounded<br> the LV for at least the 50% and all the other slice; B) all the slices where the Myo completely surrounded the LV. To evaluate the clinical relevance of the proposed architecture in a practical use case scenario, a graphical user interface was developed to allow clinicians to revise, and correct when needed, the automatic segmentation. Additionally, to assess generalizability, analysis of CMR images obtained in 12 healthy volunteers (DB2) with different equipment (Siemens, 3T) and settings was performed.<br> Results: The proposed architecture outperformed the original U-Net. Comparing the performance on DB1 between the two criteria, no significant differences were measured when considering all slices together, but were present when only basal slices were examined. Automatic and manually-adjusted segmentation<br> performed similarly compared to the GS (bias±95%LoA): LVEDV -1±12 ml, LVESV -1±14 ml, RVEDV 6±12 ml, RVESV 6±14 ml, ED LV mass 6±26 g, ES LV mass 5±26 g). Also, generalizability showed very similar performance, with Dice scores of 0.944 (LV), 0.908 (RV) and 0.852 (Myo) on DB1, and 0.940 (LV), 0.880 (RV), and 0.856 (Myo) on DB2.<br> Conclusions: Our results support the potential of DL methods for accurate LV and RV contours segmentation and the advantages of dense skip connections in alleviating the semantic gap generated when high level features are concatenated with lower level feature. The evaluation on our dataset, considering separately the performance on basal and apical slices, reveals the potential of DL approaches for fast, accurate and reliable automated cardiac segmentation in a real clinical setting.<br> </p>
Dataset related to article "Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT"
<p>This record contains raw data related to article "Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT"</p> <p>Aim:</p> <p>To develop an algorithm, based on convolutional neural network (CNN), for the classification of lung cancer lesions as T1-T2 or T3-T4 on staging fluorodeoxyglucose positron emission tomography (FDG-PET)/CT images.</p> <p>Methods:</p> <p>We retrospectively selected a cohort of 472 patients (divided in the training, validation, and test sets) submitted to staging FDG-PET/CT within 60 days before biopsy or surgery. TNM system seventh edition was used as reference. Postprocessing was performed to generate an adequate dataset. The input of CNNs was a bounding box on both PET and CT images, cropped around the lesion centre. The results were classified as Correct (concordance between reference and prediction) and Incorrect (discordance between reference and prediction). Accuracy (Correct/[Correct + Incorrect]), recall (Correctly predicted T3-T4/[all T3-T4]), and specificity (Correctly predicted T1-T2/[all T1-T2]), as commonly defined in deep learning models, were used to evaluate CNN performance. The area under the curve (AUC) was calculated for the final model.</p> <p>Results:</p> <p>The algorithm, composed of two networks (a "feature extractor" and a "classifier"), developed and tested achieved an accuracy, recall, specificity, and AUC of 87%, 69%, 69%, and 0.83; 86%, 77%, 70%, and 0.73; and 90%, 47%, 67%, and 0.68 in the training, validation, and test sets, respectively.</p> <p>Conclusion:</p> <p>We obtained proof of concept that CNNs can be used as a tool to assist in the staging of patients affected by lung cancer.</p>
LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Network
<p>Dataset related to the paper:<br> "LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks'' under review in Robotics and Autonomous Systems 2018 </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.