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921 results for “neural networks”
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>
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>
Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems"
<p>Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems". The corresponding scripts can be accessed on GitHub (https://github.com/axgoujon/convex_ridge_regularizers).</p> <p>The data is organized as follows:</p> <p>- ct_data_sets.tar.gz: contains preprocessed validation (aka calibration) and test sets with:</p> <ul> <li>ground truth images,</li> <li>FBP reconstructions,</li> <li>measurements, for the various settings explored (3 noise levels).</li> </ul> <p>- mri_data_sets.tar.gz: contains preprocessed validation (aka calibration) and test sets with:</p> <ul> <li>subsampling cartesian masks,</li> <li>sensitivity masks,</li> <li>ground truth image,</li> <li>measurements, for the various settings explored: single- and multi-coil MRI, various acceleration rates (2, 4, and 8), synthetic noise, and different image types (fat suppression or not).</li> </ul> <p>For completeness, the code used to generate the preprocessed data from the raw data can be found on the GitHub repository.</p>
Isogenic hiPSC models of Turner syndrome development reveal shared roles of inactive X and Y in the human cranial neural crest network
GEO Series GSE264742. Homo sapiens. 52 samples. Type: Expression profiling by high throughput sequencing.
Joint sequence and chromatin neural networks characterize the differential abilities of Forkhead transcription factors to engage inaccessible chromatin (RNA-seq)
GEO Series GSE244408. Mus musculus. 17 samples. Type: Expression profiling by high throughput sequencing.
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.
SEAMoD: A fully interpretable neural network for cis-regulatory analysis of differentially expressed genes [RNA-seq]
GEO Series GSE236448. Mus musculus. 11 samples. Type: Expression profiling by high throughput sequencing.
SEAMoD: A fully interpretable neural network for cis-regulatory analysis of differentially expressed genes [ChIP-seq]
GEO Series GSE236449. Mus musculus. 13 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
SEAMoD: A fully interpretable neural network for cis-regulatory analysis of differentially expressed genes
GEO Series GSE236450. Mus musculus. 24 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Neural Network-Assisted Humanization of COVID-19 Hamster scRNAseq Data Reveals Matching Severity States in Human Disease
GEO Series GSE253845. Phodopus roborovskii. 15 samples. Type: Expression profiling by high throughput sequencing.
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>
Data and codebase for "Why do recurrent neural networks suddenly learn? Bifurcation mechanisms in neuro-inspired short-term memory tasks"
Open the record for dataset details and reuse information.
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>
Predicting Hotel Booking Cancellations using Tree-based Neural Networks
<p>The datasets provide booking data of a hotel chain.</p>
Joint sequence and chromatin neural networks characterize the differential abilities of Forkhead transcription factors to engage inaccessible chromatin (ATAC-seq)
GEO Series GSE244409. Mus musculus. 12 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Supplementary Materials for Sensuator: A Hybrid Sensor-Actuator Approach to Soft Robotic Proprioception Using Recurrent Neural Networks
<p>Videos accompanying the paper Sensuator: A Hybrid Sensor-Actuator Approach to Soft Robotic Proprioception Using Recurrent Neural Networks</p>
Simple Feedforward Artificial Neural Network
<p><br /> Simple Feedforward Artificial Neural Network</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>
Trained Artificial Neural Network for Detecting Cut-off low related Vb-Cyclones in a large single-model ensemble
<p>Trained network data accompanying the research letter "Detecting Climate Change Effects on Vb-Cyclones in a 50-Member Single-Model Ensemble Using Machine Learning" submitted to Geophysical Research Letters.</p> <p>Licence: Creative Commons Attribution-NonCommercial-No Derivatives 4.0 International (CC BY-NC-ND 4.0)</p>
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.