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ShareScore release 0.7.1
Dataset results
58 results for “Feature Extraction”
Imagenet Features Extracted with VGG-19
<p>This dataset contains features extracted from the Imagenet dataset using a pre-trained VGG-19 neural network. The network was configured with an input layer of (200, 200, 3). Feature extraction was performed using the Python package <a href="https://github.com/jgoodman8/py-image-features-extractor" target="_blank" rel="nofollow noopener noreferrer">Py Image Feature Extractor</a>.</p>
Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions
<p>We uploaded the 15 morphological features of 91 samples of 85 patients analyzed in the manuscript: Fusco, Roberta, Adele Piccirillo, Mario Sansone, Vincenza Granata, Paolo Vallone, Maria L. Barretta, Teresa Petrosino, Claudio Siani, Raimondo Di Giacomo, Maurizio Di Bonito, Gerardo Botti, and Antonella Petrillo. 2021. "Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions" Applied Sciences 11, no. 4: 1880. https://doi.org/10.3390/app11041880</p>
Selfee: Self-supervised features extraction of animal behaviors
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Transfer Learning for leveraging computer vision in infrastructure maintenance [extracted features]
<p>Dataset containing features extracted from images taken on single case of infrastructure facility for the purpose of training Transfer Learned CNN classifier. It is meant to be used with KrakN framework (https://github.com/MatZar01/KrakN), published with the research paper.</p>
Features detected in methanolic extracts of marine invertebrates.
<p>Data table.</p>
The EMG source data, data processing code and feature extraction source code
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DMSA Extracted Features
<p>Pre-computed features for DMSA (https://github.com/stefan-balke/dmsa).</p>
Dataset of "Towards Automatic Feature Extraction and Sample Generation of Grain Structure by Variational Autoencoder"
<p><em>No description.</em></p>
Supplementary data to the paper: Automatic Extraction of Anthropometric Features for the Individualization of the Pinna-Related Transfer Function in the Median Plane
<p>Supplementary research data to the paper (rejected):</p> <blockquote> <p>Davide Fantini, Federico Avanzini, Stavros Ntalampiras and Giorgio Presti (2023) "Automatic Extraction of Anthropometric Features for the Individualization of the Pinna-Related Transfer Function in the Median Plane"</p> </blockquote> <p>The repository includes the research data generated and analyzed in the abovementioned paper describing a method for PRTF individualization. In particular, the following data are included:</p> <ul> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/README.md">README.md</a>: instructions for the data</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/pinna_range_img.mat">pinna_range_img.mat</a>: pinna range images extracted from the 3D head meshes of the <a href="https://depositonce.tu-berlin.de/items/dc2a3076-a291-417e-97f0-7697e332c960">HUTUBS dataset</a></li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/landmarks.mat">landmarks.mat</a>: landmarks coordinates both manually annotated and automatically placed with ASM</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/anthropometry.mat">anthropometry.mat</a>: anthropometric parameters automatically extracted from both manually annotated and ASM-fitted landmarks</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/img_features.mat">img_features.mat</a>: image features pinna cavities extracted from both manually annotated and ASM-fitted landmarks</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/grnn_models.mat">grnn_models.mat</a>: Generalized Regression Neural Network (GRNN) models trained from both HUTUBS anthropometry and the proposed pinna features</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/predicted_dtf.mat">predicted_dtf.mat</a>: Directional Transfer Function (DTF) sets predicted from both HUTUBS anthropometry and the proposed pinna features</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/anthropometry_documentation.pdf">anthropometry_documentation.pdf</a>: documentation of the pinna anthropometric parameters</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/auditory_model_complete_elevation_range.pdf">auditory_model_complete_elevation_range.pdf</a>: auditory model evaluation in the complete elevation range</li> </ul> <p>The data are provided in the Matlab file format MAT. Nevertheless, the MAT files can be read with other programming languages, such as Python (<a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">scipy.io.loadmat</a>).</p> <p>A GitHub repository to automatically extract the pinna landmarks and features as described in the paper is available <a href="https://github.com/DavideFantini/pinna-landmarks-fitting-and-anthropometry-extraction" target="_blank" rel="noopener">here</a>.</p>
BradyXplore Phase II: Bradykinesia Feature Extraction System
ClinicalTrials.gov study NCT02358876. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors
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Imagenet Features Extracted with LBP
<p>This dataset contains features extracted from the Imagenet dataset using Local Binary Patterns Histograms. The LBP algorithm is configured with params <em>p=8</em>, <em>r=1</em>, <em>gridX=8</em> and <em>gridY=8</em>. Feature extraction was performed using the Python package <a href="https://github.com/jgoodman8/py-image-features-extractor" target="_blank" rel="nofollow noopener noreferrer">Py Image Feature Extractor</a>.</p>
Research on the Extraction of Tongue and Facial Diagnosis Features of Psoriasis Vulgaris in Traditional Chinese Medicine and Its Correlation With Laboratory Indicators
ClinicalTrials.gov study NCT07351448. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Artificial Intelligence-enabled Large-scale Electrocardiogram Feature Extraction and Exploring Association Between the Extracted Features and Mortality, Stroke or Various Health Outcome of Interest
ClinicalTrials.gov study NCT06179849. IPD Sharing: NO. Countries: 1. Publications: 0.
The Study of Slip Pulse Feature Extraction Method Based on the Image Processing Technology for Traditional Chinese Medicine
ClinicalTrials.gov study NCT04392687. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Time series from "Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series Forecasting on a Level-Playing Field"
<p>Time Series used in the evaluation of "Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series Forecasting on a Level-Playing Field"</p>
Dataset and code for "FK-means: Automatic Atrial Fibrosis Segmentation using Fractal-guided K-means Clustering with Voronoi-Clipping Feature Extraction of Anatomical Structures": FKmeans for fibrosis segmentation
<p>Assessment of left atrial (LA) fibrosis from late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) adds to the management of patients with atrial fibrillation (AF). However, accurate assessment of fibrosis in the LA wall remains challenging. Excluding anatomical structures in the LA proximity using clipping techniques can reduce misclassification of LA fibrosis. A novel FK-means approach for combined automatic clipping and automatic fibrosis segmentation was developed. This approach combines a feature-based Voronoi diagram with a hierarchical 3D K-means fractal-based method. The proposed automatic Voronoi clipping method was applied on LGE MRI data and achieved a Dice score of 0.75, similar as the score obtained by a deep learning method (3D UNet) for clipping (0.74). The automatic fibrosis segmentation method, which utilizes the Voronoi clipping method, achieved a Dice score of 0.76. This outperformed a 3D U-Net method for clipping and fibrosis classification, which had a Dice score of 0.69. Moreover, the proposed automatic fibrosis segmentation method achieved a Dice score of 0.90, using manual clipping of anatomical structures. The findings suggest that the automatic FK-means analysis approach enables reliable LA fibrosis segmentation and that clipping of anatomical structures in the atrial proximity can add to the assessment of atrial fibrosis. </p>
MRI dataset for susceptibility-based radiomic feature extraction in healthy controls and patients with multiple sclerosis
<p>This dataset include multiparametric MRI brain images and susceptiblity-based radiomic features within a mixed sample of 100 patients with multiple sclerosis and 50 healthy controls. We focused on the normal appearing white matter and its tracts. <br>Imaging and radiomic data are organized according to the Brain Imaging Directory Structure (BIDS) and for each subjects the following data are provided: anatomical T1w and T2w images; DWI after the correction for EPI distortions and susceptibility effects, eddy currents, and signal dropout, b-values and b-vectors and registration matrix to T1w; QSM QSM reconstruction, already registered in T1w space, raw magnitude and phase maps for the 5 echo times and registration matrix to T1w; volumes of interest (normal appearing white matter and tracts, divided for the two hemispheres); 107 radiomic features for each volume of interest. All images were anonymized and de-identified. <br>Additionally, in the database there is the ‘code’ folder containing the available scripts used for the processing (image registration, radiomic feature extraction, and robustness evaluation) and the participants file (containing for each subject the ID number, age, sex, date of scan, clinical condition (‘HC’ and ‘MS’) and the of the DWI sequence used, i.e. single- or multi-shell) and the dataset description.</p> <p>Data were provided by the Functional and Molecular Neuroimaging Unit at IRCCS Istituto delle Scienze Neurologiche di Bologna, Italy. <a href="https://doi.org/10.5281/zenodo.11278906">Here</a> you can find the Data Use Agreement (DUA) to sign before using the dataset, together with the instructions to have the access.</p> <p>More details about image acquisition and processing may be found in Fiscone et al. (2024) Multiparametric MRI database for susceptibility-based radiomic feature extraction and analysis, <em>Scientific Data</em>. Please cite this paper if you use this dataset for publications or presentations. For any further information, please email cristiana.fiscone@gmail.com, davidneil.manners@unibo.it or giovanni.sighinolfi3@unibo.it. </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.