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restrictedIPD-UNDECIDEDFeb 2026View details →

Deep-learning Enabled Ultrasound Diagnosis of Anterior Talofibular Ligament Injury

ClinicalTrials.gov study NCT06373029. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →

Deep-learning For Ultrasound Classification of Anterior Talofibular Ligament Injury

ClinicalTrials.gov study NCT06372873. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.

Evaluation of a Free-breathing Cardiac Cine-MRI Sequence With Image Reconstructions by Deep-Learning in Ischemic Heart Disease

ClinicalTrials.gov study NCT05105984. IPD Sharing: NO. Countries: 1. Publications: 1.

Clinical Research on a Novel Deep-learning Based System in Pancreatic Mass Diagnosis

ClinicalTrials.gov study NCT04607720. IPD Sharing: NO. Countries: 1. Publications: 1.

opencc-zeroJan 2021View details →

dryad28/100

Data from: Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain

We have developed an open-source software called BIRDS (bi-channel image registration and deep-learning segmentation) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image pre-processing, bi-channel registration, automatic annotation, creation of a 3D digital frame, high-resolution visualization, and expandable quantitative analysis. This new bi-channel registration algorithm is adaptive to various types of whole-brain data from different microscopy platforms and shows dramatically improved registration accuracy. Additionally, as this platform combines registration with neural networks, its improved function relative to other platforms lies in the fact that the registration procedure can readily provide training data for network construction, while the trained neural network can efficiently segment incomplete/defective brain data that is otherwise difficult to register. Our software is thus optimized to enable either minute-timescale registration-based segmentation of cross-modality, whole-brain datasets or real-time inference-based image segmentation of various brain regions of interest. Jobs can be easily submitted and implemented via a Fiji plugin that can be adapted to most computing environments.

opencc-by-4.0Aug 2024View details →

Estimation of Ca2+ wet deposition in the Northern Hemisphere by use of CNN deep-learning model

A dataset to estimate long-term and high-resolution gridded Ca2+ wet deposition across the Northern Hemisphere from 2001-2022.

opencc-by-4.0Aug 2021View details →

Efficient, Low-cost Bridge Cracking Detection and Quantification Using Deep-learning and UAV Images

Many bridges in the State of Louisiana and the United States are working under serious degradation conditions where cracks on bridges threaten structural integrity and public security. To ensure structural integrity and public security, it is required that bridges in the US be inspected and rated every two years. Currently, this biannual assessment is largely implemented using manual visual inspection methods, which is slow and costly. In addition, it is challenging for workers to detect cracks in regions that are hard to reach, e.g., the top part of the bridge tower, cables, mid-span of the bridge girders, and decks. This research develops an efficient low-cost deep learning-based methodology to identify cracks on bridges using computer vision-based techniques and deep learning. The Convolutional Neural Networks (CNN) deep learning method is used to identify cracks from images. In this research, a programmable drone is developed that can fly along a pre-defined trajectory. A large volume of images was collected from local bridges and pavements using drones. The collected images were preprocessed and divided into around forty thousand 256 by 256-pixel sub-images and fed into the CNN model. Data augmentation techniques are applied to increase the number of images in some cases. Parameters of the selected CNN model were optimized to obtain the best configuration. To evaluate the performance of the method, images from a different local bridge were used for testing. Research results show that with the optimized CNN model, cracks in the images can be identified efficiently and accurately. The developed methodology can also category the cracked image as slight, moderate, or severe cracking based on a pre-defined quantification index. The research outcome of this project has the potential to automate crack damage identification of bridge key components in a cost-effective manner. Also, the developed methodology is expected to facilitate crack damage identification for other transportation infrastructures, e.g., pavement and traffic sign structures.

opencc-by-4.0Sep 2022View details →

Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods

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 Zamioculcas zamiifolia and Solanum lycopersicum (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. Data repository for our paper " Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods ", submitted to the journal Bioinspiration & Biomimetics . Please refer to the paper for more information.   Contents of this repository <ul> <li>mu_interface: 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>SupplementaryCode: Includes the discriminant analysis classifier, raw datasets, calculated features, test-train split  and the corresponding code.</li> <li>dl-4-tsc: 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>DeepClassifier: Trained deep learning time series classifier.</li> <li>classification_results.xlsx: 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>

opencc-by-4.0Dec 2023View details →

Figure 7 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept

Figure 7. – Example of detection and classification obtained with an image including Crinoïds, a Gastropod and pieces of seaweed with network 2; red squares and annotations have been provided by the computer with no human action.

opencc-by-4.0Sep 2024View details →

Supplementary material - Non-resonant background removal in broadband CARS microscopy using deep-learning algorithms

This repository contains the data presented in the publication: "Non-resonant background removal in broadband CARS microscopy using deep-learning algorithms".

opencc-by-4.0Nov 2024View details →

Spatially resolved transcriptomics and graph-based deep-learning improve accuracy of routine CNS tumor diagnostics

The diagnostic landscape of brain tumors has recently evolved to integrate comprehensive molecular markers alongside traditional histopathological evaluation. Foremost, genome-wide DNA methylation profiling and next generation sequencing (NGS) has become a cornerstone in classifying Central Nervous System (CNS) tumors, as recognized by its inclusion into the 2021 WHO classification. Despite its diagnostic precision, a limiting requirement for NGS and methylation profiling is sufficient DNA quality and quantity which restricts its feasibility, especially in cases with small biopsy samples or low tumor cell content, both frequent challenges in specimen of diffusely growing CNS lesions. Addressing these challenges, we demonstrate a application, namely NePSTA (NeuroPathology Spatial Transcriptomic Analysis), which is capable of generating comprehensive morphological and molecular neuropathological diagnostics from single 5 µm tissue sections. Our framework employs 10x Visium spatial transcriptomics with graph neural networks for automated histological and molecular evaluations. Trained and evaluated across 130 patients with CNS malignancies and healthy donors across four medical centers, NePSTA integrates spatial gene expression data and inferred CNAs to predict tissue histology and methylation-based subclasses with high accuracy. Further, we demonstrate the ability to reconstruct immunohistochemistry and genotype profiling on single thin slides of minute tissue biopsies. Our approach has minimal tissue requirements, often inadequate for conventional molecular diagnostics, demonstrating the potential to transform neuropathological diagnostics and enhance tumor subtype identification with implications for fast and precise diagnostic work-up.

ClinicalTrials.gov28/100

Clinical Research on a Novel Deep-learning Based System in Pancreatic Endoscopic Ultrasound Scanning

ClinicalTrials.gov study NCT05792267. IPD Sharing: NO. Countries: 1. Publications: 0.

ClinicalTrials.gov28/100

Clinical Research on a Novel Deep-learning Based System in Mediastinal Endoscopic Ultrasound Scanning

ClinicalTrials.gov study NCT05792280. IPD Sharing: NO. Countries: 1. Publications: 0.