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Dataset results
61 results for “Deep learning classification”
Deep-learning For Ultrasound Classification of Anterior Talofibular Ligament Injury
ClinicalTrials.gov study NCT06372873. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Study on Classification Method of Indocyanine Green Lymphography Based on Deep Learning
ClinicalTrials.gov study NCT04824378. IPD Sharing: NO. Countries: 1. Publications: 4.
Image-based taxonomic classification of bulk biodiversity samples using deep learning and domain adaptation
Open the record for dataset details and reuse information.
Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods
<p>The physiology of living organisms, such as living plants, is complex and particularly difficult to understand on a macroscopic, organism-holistic level. Among the many options for studying plant physiology, electrical potential and tissue impedance are arguably simple measurement techniques that can be used to gather plant-level information. Despite the many possible uses, our research is exclusively driven by the idea of phytosensing, that is, interpreting living plants’ signals to gather information about surrounding environmental conditions. As ready-to-use plant-level physiological models are not available, we consider the plant as a blackbox and apply statistics and machine learning to automatically interpret measured signals. In simple plant experiments, we expose <em>Zamioculcas zamiifolia</em> and <em>Solanum lycopersicum</em> (tomato) to four different stimuli: wind, heat, red light and blue light. We measure electrical potential and tissue impedance signals. Given these signals, we evaluate a large variety of methods from statistical discriminant analysis and from deep learning, for the classification problem of determining the stimulus to which the plant was exposed. We identify a set of methods that successfully classify stimuli with good accuracy, without a clear winner. The statistical approach is competitive, partially depending on data availability for the machine learning approach. Our extensive results show the feasibility of the blackbox approach and can be used in future research to select appropriate classifier techniques for a given use case. In our own future research, we will exploit these methods to derive a phytosensing approach to monitoring air pollution in urban areas.</p> <p>Data repository for our paper " <em>Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods</em> ", submitted to the journal Bioinspiration & Biomimetics . Please refer to the paper for more information.</p> <p> </p> <p><strong>Contents of this repository</strong></p> <ul> <li><em>mu_interface:</em> Code for our data collection plant experiments, based on Raspberry Pis and the <a href="http://cybertronica.co/?q=products/phytosensor">Cybertronica phytosensing and phytoactuating system</a>.</li> <li><em>SupplementaryCode</em>: Includes the discriminant analysis classifier, raw datasets, calculated features, test-train split and the corresponding code.</li> <li><em>dl-4-tsc:</em> Deep learning framework developed by <a href="https://doi.org/10.1007/s10618-019-00619-1">Fawaz et. al (Deep learning for time series classification: a review)</a> and adapted to our use case. </li> <li><em>DeepClassifier: </em>Trained deep learning time series classifier.</li> <li><em>classification_results.xlsx: </em>Overview of the results from the deep learning framework (accuracy, precision, recall, training time, confusion matrix) and the achieved accuracies using discriminant analysis with sequential forward section (further evaluation metrics of the discriminant analysis can be found in SupplementaryCode.</li> </ul>
A Deep Learning-Based Approach for Efficient Detection and Classification of Local Ca²⁺ Release Events in Full-Frame Confocal Imaging
<p>U-Net implementation and project code: <a href="https://github.com/dottipr/sparks_project">https://github.com/dottipr/sparks_project</a></p> <p>GUI's github repository: <a href="https://github.com/r-janicek/xytCalciumSignalsDetection">https://github.com/r-janicek/xytCalciumSignalsDetection</a></p>
Ultra-fast deep-learned pediatric CNS tumor classification
GEO Series GSE237874. Homo sapiens. 5 samples. Type: Methylation profiling by high throughput sequencing; Other.
miRBind: a Deep Learning Method for miRNA Binding Classification
GEO Series GSE218466. Homo sapiens. 2 samples. Type: Other.
Deep Learning-Based Multimodal Clustering Model for Endotyping and Post-Arthroplasty Response Classification using Knee Osteoarthritis Subject-Matched Multi-Omic Data
GEO Series GSE222979. Homo sapiens. 1242 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Self-supervised retinal thickness prediction enables deep learning from unlabeled data to boost classification of diabetic retinopathy
<p><strong>This data repository contains the OCT images and binary annotations for segmentation of retinal tissue using deep learning. To use, please refer to the Github repository </strong><a href="https://github.com/theislab/DeepRT">https://github.com/theislab/DeepRT</a>.</p> <p> </p> <p><strong>#######</strong></p> <p><strong>Access to large, annotated samples represents a considerable challenge for training accurate deep-learning models in medical imaging. While current leading-edge transfer learning from pre-trained models can help with cases lacking data, it limits design choices, and generally results in the use of unnecessarily large models. We propose a novel, self-supervised training scheme for obtaining high-quality, pre-trained networks from unlabeled, cross-modal medical imaging data, which will allow for creating accurate and efficient models. We demonstrate this by accurately predicting optical coherence tomography (OCT)-based retinal thickness measurements from simple infrared (IR) fundus images. Subsequently, learned representations outperformed advanced classifiers on a separate diabetic retinopathy classification task in a scenario of scarce training data. Our cross-modal, three-staged scheme effectively replaced 26,343 diabetic retinopathy annotations with 1,009 semantic segmentations on OCT and reached the same classification accuracy using only 25% of fundus images, without any drawbacks, since OCT is not required for predictions. We expect this concept will also apply to other multimodal clinical data-imaging, health records, and genomics data, and be applicable to corresponding sample-starved learning problems.</strong></p> <p><strong>#######</strong></p>
Datasets corresponding to publication: AIDeveloper: deep learning image classification in life science and beyond
<p>Datasets and videos corresponding to publication:<br> AIDeveloper: deep learning image classification in life science and beyond</p>
A high accuracy of deep learning based CNN architecture: classic, VGGNet, and RestNet50 for Covid-19 image classification
Open the record for dataset details and reuse information.
Data for "LAND COVER CLASSIFICATION FROM A MAPPING PERSPECTIVE: PIXELWISE SUPERVISION IN THE DEEP LEARNING ERA"
<p><strong>Contents</strong></p> <ul> <li>clc_maps.zip contains the dataset.</li> <li>LICENSE.txt describes the usage terms of the maps.</li> </ul> <p>The maps contained in clc_maps.zip follow the naming convention of BigEarthNet [1], i.e. each sample of BigEarthNet has a corresponding pixel-level label map in the dataset.</p> <p>[1] G. Sumbul, M. Charfuelan, B. Demir, and V. Markl, “Bigearthnet: A large-scale benchmark archive for remote sensing image understanding,” in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019, pp. 5901–5904.</p> <p><strong>Description</strong></p> <p>The original shape file (<a href="https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download">link</a>) was altered by reprojecting the shape file onto the coordinate reference system (CRS) of the respective BigEarthNet sample images to ensure pixel synchronicity. Afterwards, the shapes present in the sample CRS are rasterized by burning a linearly increasing class index which replaces the textual CLC nomenclature. The class IDs and their corresponding class names are presented in the following section. </p> <p><strong>Classes</strong></p> <p>Class ID - Corine Land Cover 2018 class name<br> 1 - Continuous urban fabric<br> 2 - Discontinuous urban fabric<br> 3 - Industrial or commercial units<br> 4 - Road and rail networks and associated land<br> 5 - Port areas<br> 6 - Airports<br> 7 - Mineral extraction sites<br> 8 - Dump sites<br> 9 - Construction sites<br> 10 - Green urban areas<br> 11 - Sport and leisure facilities<br> 12 - Non-irrigated arable land<br> 13 - Permanently irrigated land<br> 14 - Rice fields<br> 15 - Vineyards<br> 16 - Fruit trees and berry plantations<br> 17 - Olive groves<br> 18 - Pastures<br> 19 - Annual crops associated with permanent crops<br> 20 - Complex cultivation patterns<br> 21 - Land principally occupied by agriculture, with significant areas of natural vegetation<br> 22 - Agro-forestry areas<br> 23 - Broad-leaved forest<br> 24 - Coniferous forest<br> 25 - Mixed forest<br> 26 - Natural grasslands<br> 27 - Moors and heathland<br> 28 - Sclerophyllous vegetation<br> 29 - Transitional woodland-shrub<br> 30 - Beaches, dunes, sands<br> 31 - Bare rocks<br> 32 - Sparsely vegetated areas<br> 33 - Burnt areas<br> 34 - Glaciers and perpetual snow<br> 35 - Inland marshes<br> 36 - Peat bogs<br> 37 - Salt marshes<br> 38 - Salines<br> 39 - Intertidal flats<br> 40 - Water courses<br> 41- Water bodies<br> 42 - Coastal lagoons<br> 43 - Estuaries<br> 44 - Sea and ocean<br> 48 - NODATA<br> 49 - UNCLASSIFIED LAND SURFACE<br> 50 - UNCLASSIFIED WATER BODIES </p> <p>More details about the CLC classes and conventions can be found in the CLC nomenclature guide (<a href="https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html">Link</a>).</p> <p><strong>Attribution</strong></p> <p>If you find this work useful please consider citing:</p> <p>Wilhelm, T.; Koßmann, D. LAND COVER CLASSIFICATION FROM A MAPPING PERSPECTIVE: PIXELWISE SUPERVISION IN THE DEEP LEARNING ERA. In Proceedings of the IGARSS 2021—2021 IEEE International Geoscience and Remote Sensing Symposium, Brussels, Belgium, 12 – 16 July 2021; to appear.</p> <p><strong>License</strong></p> <p>The generated maps are based on data from the Copernicus program, which are subject to the terms described here:<br> <a href="https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=metadata">https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=metadata</a></p>
Supplementary Video 1 of: Ultra-fast deep-learned CNS tumor classification during surgery
<p><strong>Supplementary Video 1:</strong> An intraoperative sequencing experiment (INTRA_4) was captured on film. Audio has been redacted for privacy reasons. The timeline of this specific experiment is shown as a representative example in Fig. 4.</p>
A Deep Learning Approach to Submerged Teeth Classification and Detection
ClinicalTrials.gov study NCT04309851. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Deep Learning Model for Pure Solid Nodules Classification
ClinicalTrials.gov study NCT05542992. IPD Sharing: NO. Countries: 1. Publications: 0.
Deep Learning for Histopathological Classification and Prognostication of Gynaecologic Smooth Muscle Tumours
ClinicalTrials.gov study NCT06540846. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Deep Learning for Classification of Scheimpflug Corneal Tomography Images
ClinicalTrials.gov study NCT04497207. IPD Sharing: NO. Countries: 1. Publications: 0.
Pathological Classification of Pulmonary Nodules in Images Using Deep Learning
ClinicalTrials.gov study NCT05221814. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Deep-learning Based Classification of Spine CT
ClinicalTrials.gov study NCT03790930. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Deep Learning-based Classification and Prediction of Radiation Dermatitis in Head and Neck Patients
ClinicalTrials.gov study NCT05607225. IPD Sharing: NO. Countries: 1. Publications: 0.
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.