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Dataset results
20 results for “Multimodal fusion”
M3-OCTA:Leveraging Multimodal Fusion for Enhanced Diagnosis of Multiple Retinal Diseases in Ultra-wide OCTA
<p>Ultra-wide optical coherence tomography angiography (UW-OCTA) is an emerging imaging technique that offers significant advantages over traditional OCTA by providing an exceptionally wide scanning range of up to 24 x 20 mm^{2}, covering both the anterior and posterior regions of the retina. However, the currently accessible UW-OCTA datasets suffer from limited comprehensive hierarchical information and corresponding disease annotations. To address this limitation, we have curated the pioneering M3OCTA dataset, which is the first multimodal (i.e., multilayer), multi-disease, and widest field-of-view UW-OCTA dataset. Furthermore, the effective utilization of multi-layer ultra-wide ocular vasculature information from UW-OCTA remains underdeveloped. To tackle this challenge, we propose the first cross-modal fusion framework that leverages multi-modal information for diagnosing multiple diseases. Through extensive experiments conducted on our openly available M3OCTA dataset, we demonstrate the effectiveness and superior performance of our method, both in fixed and varying modalities settings. The construction of the M3OCTA dataset, the first multimodal OCTA dataset encompassing multiple diseases, aims to advance research in the ophthalmic image analysis community.</p> <p>Our proposed M3OCTA is the first multi-modal based ultra-wide retinal OCTA dataset, involving 1637 scans from 1046 eyes of 620 individuals imaged in Zigong First People’s Hospital through 24×20 scan mode. Specifically, 1067 scans contains choroid large vessel image; images of 1310 scans from 496 people are labeled as six classes in multi-label setting, including healthy, diabetic retinopathy (DR), diabetic macular edema (DME), Retinal Vein Occlusion (RVO), Hypertension (HBP) and Vitreous Hemorrhage (VH), and then split into train, validation and test set as 6:2:2. The remaining unlabeled data are only used in the pretraining step. Details of our M3OCTA and other public ones are listed in Table.1. Compared with others, M3OCTA dataset demonstrates superiorities in several aspects including the number of modalities, number of patients, image resolution, and FOV.</p> <p> </p> <p><strong>You can request this dataset through signing the attached agreement. The download link will send to you. </strong></p>
GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion (presentation recording)
<p>Video recording of the presentation for the publication N. Souli et al., "GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion," 2020 22nd International Conference on Transparent Optical Networks (ICTON), Bari, Italy, 2020, pp. 1-4, doi: 10.1109/ICTON51198.2020.9203087.</p>
Multimodal Fusion of Liquid Biopsy and CT Enhances Differential Diagnosis of Early-stage Lung Adenocarcinoma
<p>This research explores the potential of multimodal fusion for the differential diagnosis of early-stage lung adenocarcinoma (LUAD) (tumor sizes < 2 cm). It combines liquid biopsy biomarkers, specifically extracellular vesicle long RNA (evlRNA) and the computed tomography (CT) attributes.</p><p>It includes 4 files:</p><p>1. gene expression matrix : gene expression matrix for all samples (normalized read counts) .</p><p>2. evlRNA Rad features: 6 image features, 17 evlRNA features and labels</p><p>3. differentially expressed genes: Differentially expressed genes in LUAD patients compared with Benign individuals.</p><p>4. raw data: raw data used to for figure and table.</p>
Perceived Mental Workload Classification using Intermediate Fusion Multimodal Deep Learning
<p><em>This repository contains all code -from data collection to perceived mental workload classification- used in the "Perceived Mental Workload Classification using Intermediate Fusion Multimodal Deep Learning" research and serves as supplementary material. <strong>A clear README file is provided, please refer to that for information about the individual scripts and their usage. </strong></em></p> <p>Mental workload detection has been attempted using various bio-signals. Recently, deep learning has allowed for novel methods and results within the BCI community. However, studies currently often only use a single modality to classify mental workload, whereas a plethora of modalities have proven to be valuable in this task.</p> <p>A dataset on which these scripts was also made publicly available under the following DOI: 10.4121/12932801<br> This dataset contains data collected during research into mental workload (MWL) detection using deep learning. It is being made public as supplementary data for publications, as well as for reuse in research that seeks to classify MWL using multimodal physiological data. The goal of this repository and dataset is to serve as a testing ground for the creation of deep neural networks that can classify MWL using multimodal physiological data.</p> <p>The data in this dataset was collected in the Behavioural, Management, and Social Sciences Lab, University of Twente, Enschede, The Netherlands in June/July 2020.</p>
Multimodal omics data fusion for cancer prognosis with co-attention-based variational autoencoder
Open the record for dataset details and reuse information.
The dataset of "Deep Learning-enabled 3D Multimodal Fusion of Cone-Beam CT and Intraoral Mesh Scans for Clinically Applicable Tooth-bone Reconstruction"
<p>The dataset used in the study "Deep Learning-enabled 3D Multimodal Fusion of Cone-Beam CT and Intraoral Mesh Scans for Clinically Applicable Tooth-bone Reconstruction" is available upon request. Please contact the authors of the study or the responsible institution for access to the dataset.</p>
Blast Furnace Raw Material Granularity Recognition Model Based on Deep Learning and Multimodal Fusion of 3D Point Cloud
<p>Provide data code</p>
PSMA-PET/MRI-Ultrasound Multimodal Fusion Navigation for Da Vinci Robot-Assisted Radical Prostatectomy: A Randomized Controlled Trial
ClinicalTrials.gov study NCT07272317. IPD Sharing: NO. Countries: 1. Publications: 4.
Effectiveness of Retrolaminar Block in Lumbar Spine Fusion With Multimodal Analgesia
ClinicalTrials.gov study NCT07291388. IPD Sharing: NO. Countries: 0. Publications: 13.
Multimodal single-cell analyses reveal distinct fusion-regulated transcriptional programs in Ewing sarcoma.
<p>All processed data for analysis at <a href="https://github.com/furlan-lab/EwS_multiome">https://github.com/furlan-lab/EwS_multiome</a></p>
Development and Prospective Validation of a Multimodal Fusion Artificial Intelligence Model for Predicting the Efficacy of Neoadjuvant Treatment of Bladder Cancer
ClinicalTrials.gov study NCT06909643. IPD Sharing: NO. Countries: 1. Publications: 0.
Deep Learning With MRI-based Multimodal-data Fusion Enhanced Postoperative Risk Stratification of Breast Cancer
ClinicalTrials.gov study NCT06546072. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Data from: Alterations of gray and white matter networks in patients with obsessive-compulsive disorder: a multimodal fusion analysis of structural MRI and DTI using mCCA+jICA
Open the record for dataset details and reuse information.
Iono_Electron Dataset for Multimodal Fusion and Spatiotemporal Predictive Learning
<p>Data supplementary material for <em><strong>Space Weather</strong></em> journal paper with the title: Channel Mixer Layer: Multimodal Fusion Towards Machine Reasoning for Spatiotemporal Predictive Learning of Ionospheric Total Electron Content</p> <p> </p>
Development of an Artificial Intelligence System for Intelligent Pathological Diagnosis and Therapeutic Effect Prediction Based on Multimodal Data Fusion of Common Tumors and Major Infectious Diseases
ClinicalTrials.gov study NCT05046366. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Development of Multimodal Fusion Warning System and Non-invasive Techniques for Early Alzheimer's Detection.
ClinicalTrials.gov study NCT05978830. IPD Sharing: NO. Countries: 1. Publications: 0.
Construction of Early Warning Model for Pulmonary Complications Risk of Surgical Patients Based on Multimodal Data Fusion
ClinicalTrials.gov study NCT06057688. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Multimodal Endoscopic Image Fusion for Assessing Infiltration in Superficial Esophageal Squamous Cell Carcinoma
ClinicalTrials.gov study NCT06412419. IPD Sharing: NO. Countries: 1. Publications: 0.
Multimodal Analgesia vs. Routine Care Pain Management for Lumbar Spine Fusion Surgery: A Prospective Randomized Study
ClinicalTrials.gov study NCT02202369. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Multimodal Tongue-Pulse Information Fusion for Syndrome Diagnosis and Cohort Study in Children With Asthma
ClinicalTrials.gov study NCT07383883. IPD Sharing: Not stated. 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.