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173
datasets available to search
ShareScore release 0.9.0
Dataset results
173 results for “convolutional neural networks”
Convolutional Neural Network in Ovarian Follicle Identification
ClinicalTrials.gov study NCT04545918. IPD Sharing: NO. Countries: 1. Publications: 2.
Prediction of Endotracheal Tube Depth by Using Deep Convolutional Neural Networks
ClinicalTrials.gov study NCT05085743. IPD Sharing: Not stated. Countries: 1. Publications: 8.
Development of a Novel Convolution Neural Network for Arrhythmia Classification
ClinicalTrials.gov study NCT03662802. IPD Sharing: NO. Countries: 1. Publications: 14.
Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks
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Data from: Chromosome-scale inference of hybrid speciation and admixture with convolutional neural networks
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Double attention recurrent convolution neural network for answer selection
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Predicting Hydrophobicity by Learning Spatiotemporal Features of Interfacial Water Structure: Combining Molecular Dynamics Simulations with Convolutional Neural Networks
<p>Files for reproducing results from Kelkar et al. (JPCB 2020) - Predicting Hydrophobicity by Learning Spatiotemporal Features of Interfacial Water Structure: Combining Molecular Dynamics Simulations with Convolutional Neural Networks</p> <p> </p> <p>This folder contains simulations starter files and also plug-and-play datasets to test ML algorithms on molecular dynamics (MD) simulation data.</p> <p> </p> <p>All analysis scripts can also be found on GitLab on this link: https://gitlab.com/atharva-kelkar/kelkar_et_al_jpcb_2020</p>
Data from: High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: application to invasive breast cancer detection
Precise detection of invasive cancer on whole-slide images (WSI) is a critical first step in digital pathology tasks of diagnosis and grading. Convolutional neural network (CNN) is the most popular representation learning method for computer vision tasks, which have been successfully applied in digital pathology, including tumor and mitosis detection. However, CNNs are typically only tenable with relatively small image sizes (200x200 pixels). Only recently, Fully convolutional networks (FCN) are able to deal with larger image sizes (500x500 pixels) for semantic segmentation. Hence, the direct application of CNNs to WSI is not computationally feasible because for a WSI, a CNN would require billions or trillions of parameters. To alleviate this issue, this paper presents a novel method, High-throughput Adaptive Sampling for whole-slide Histopathology Image analysis (HASHI), which involves: i) a new efficient adaptive sampling method based on probability gradient and quasi-Monte Carlo sampling, and, ii) a powerful representation learning classifier based on CNNs. We applied HASHI to automated detection of invasive breast cancer on WSI. HASHI was trained and validated using three different data cohorts involving near 500 cases and then independently tested on 195 studies from The Cancer Genome Atlas. The results show that (1) the adaptive sampling method is an effective strategy to deal with WSI without compromising prediction accuracy by obtaining comparative results of a dense sampling (~6 million of samples in 24 hours) with far fewer samples (~2,000 samples in 1 minute), and (2) on an independent test dataset, HASHI is effective and robust to data from multiple sites, scanners, and platforms, achieving an average Dice coefficient of 76%.
Unexpected high accuracy of landscape genetics inference with convolutional neural networks
<p>During the last decade convolutional neural networks (CNNs) have revolutionized the application of machine learning methods to classification tasks and object recognition. These procedures can summarize with great effectiveness image data in key features that allow to classify and predict with exceptional precision. Here we show for the first time how CNNs provide highly accurate predictions of small-scale genetic differentiation and diversity in a subterranean rodent from central Argentina. Using microsatellite genotypes and high resolution satellite imagery we trained a simple CNN which was able to predict local Fst and allele diversity accounting for more than 99% of their variation. When trained with changed landscape settings the CNN still highly accounted for ~60% of variation emerging as a promising tool for population and conservation genetics.</p>
Dipper Throated Optimization with Deep Convolutional Neural Network-based Crop Classification on Remote Sensing Image Analysis
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Accurate Identification of Polyps in Screening Colonoscopies using Convolutional Neural Networks
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Data and Software: Upsampling Monte Carlo Reactor Simulation Tallies in Depleted SFR Assemblies using a Convolutional Neural Network
<p>Datasets and code used in upsampling OpenMC SFR simulation neutron flux tallies.</p>
Data and codes: Automated estimation of bioturbation intensity and ichnodiversity from the core section image using convolutional neural network
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Joint identification of groundwater contamination source and heterogeneous hydrogeological parameters in LNAPL contaminated site based on deep convolutional encoder-decoder neural networks
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dataset where a Convolutional Neural Network controls a part of the TEM optics and OAM sorter
<p>Here are uploaded the data collected when we succesfully connected a Convolutional Neural Network to the Holo-TEM in Julich. In particular the CNN controls a part of the TEM optics (beam shift x and y, C3 intensity) and the OAM sorter (the Sorter1 and Sorter 2 bias, in particular it is able to change the S1 bias to perfectly match the phase profile generated from the second sorting element).</p>
Identification of species by combining molecular and morphological data using convolutional neural networks
<p>Integrative taxonomy is central to modern taxonomy and systematic biology, including behavior, niche preference, distribution, morphological analysis, and DNA barcoding. However, decades of use demonstrate that these methods can face challenges when used in isolation, for instance, potential misidentifications due to phenotypic plasticity for morphological methods, and incorrect identifications because of introgression, incomplete lineage sorting, and horizontal gene transfer for DNA barcoding. Although researchers have advocated the use of integrative taxonomy, few detailed algorithms have been proposed. Here, we develop a convolutional neural network method (morphology-molecule network [MMNet]) that integrates morphological and molecular data for species identification. The newly proposed method (MMNet) worked better than four currently available alternative methods when tested with 10 independent data sets representing varying genetic diversity from different taxa. High accuracies were achieved for all groups, including beetles (98.1% of 123 species), butterflies (98.8% of 24 species), fishes (96.3% of 214 species), and moths (96.4% of 150 total species). Further, MMNet demonstrated a high degree of accuracy (<i>></i>98%) in four data sets including closely related species from the same genus. The average accuracy of two modest subgenomic (single nucleotide polymorphism) data sets, comprising eight putative subspecies respectively, is 90%. Additional tests show that the success rate of species identification under this method most strongly depends on the amount of training data, and is robust to sequence length and image size. Analyses on the contribution of different data types (image vs. gene) indicate that both morphological and genetic data are important to the model, and that genetic data contribute slightly more. The approaches developed here serve as a foundation for the future integration of multimodal information for integrative taxonomy, such as image, audio, video, 3D scanning, and biosensor data, to characterize organisms more comprehensively as a basis for improved investigation, monitoring, and conservation of biodiversity.</p>
Automated detection, segmentation and classification of pericardial effusions on chest CT using a deep convolutional neural network
<p>Trainingsdata for chest CT pericard effusion and the finish trained nnU-Net model. </p>
Convolutional Neural Networks in action
<p>https://x.com/Hamptonism/status/1794469409999786451</p>
Traning set for "Accurate De Novo Peptide Sequencing Using Fully Convolutional Neural Networks"
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Dataset used in "Estimating Dispersion Coefficient in Flow Through Heterogeneous Porous Media by a Deep Convolutional Neural Network" by Kamrava et al. in Geophysical Research Letters.
<p>Morphology of Heterogeneous Porous Media</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.