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97 results for “Medical Imaging”
Health sciences librarians’ awareness and incorporation of informed consent standards for medical image publication: A preliminary study
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Python and Jupyter Notebook for Medical Image Analysis - OpenMRBenelux 2020
<p>Dataset for the workshop "Python and Jupyter Notebook for Medical Image Analysis" at OpenMRBenelux - January 22, 2020 - Nijmegen (The Netherlands)</p>
A Big Data Analysis Algorithm for Massive Sensor Medical Images
<p><span>The smart sensor based big data analysis recommendation system has significant privacy and security concerns when it comes to using sensor medical images for suggestions and monitoring. The danger of security breaches and unauthorized access which might lead to identity theft and privacy violations increases when sending and storing sensitive medical data on the cloud. Insufficient or erroneous patient data can lead to poor treatment decisions, misdiagnoses, and unreliable recommendations. By creating an anomaly detection system based on machine learning specifically for medical image and providing timely treatments and notifications, our effort will improve patient care and well-being. We infer the feature extraction, feature selection, attack detection, and data collection data processing procedures in order to anticipate the anomaly in patient data. We transfer the data, take care of any missing values, and sanitize it using the data pre-processing mechanism. We employed the RFE and DPCA algorithms for feature selection and extraction, respectively. In addition, we applied the AGRNN approach to identify abnormalities. Data arrival rate, resource consumption, propagation delay, transaction epoch, true positive rate, false alarm rate, and RMSE are some of the metrics used to evaluate the proposed task.</span></p>
Multi-modality medical image dataset for medical image processing in Python lesson
<p>This dataset contains a collection of medical imaging files for use in the <a href="https://github.com/esciencecenter-digital-skills/medical-image-processing">"Medical Image Processing with Python" lesson</a>, developed by the <a href="https://www.esciencecenter.nl/">Netherlands eScience Center</a>. </p> <p>The dataset includes:</p> <ol> <li>SimpleITK compatible files: MRI T1 and CT scans (<em>training_001_mr_T1.mha, training_001_ct.mha</em>), digital X-ray (<em>digital_xray.dcm</em> in DICOM format), neuroimaging data (<em>A1_grayT1.nrrd, A1_grayT2.nrrd</em>). Data have been downloaded from <a href="https://insightsoftwareconsortium.github.io/SimpleITK-Notebooks/Python_html/00_Setup.html">here</a>. </li> <li>MRI data: a T2-weighted image (<em>OBJECT_phantom_T2W_TSE_Cor_14_1.nii</em> in NIfTI-1 format). Data have been downloaded from <a href="../records/6467772">here</a>. </li> <li>Example images for the machine learning lesson: chest X-rays (<em>rotatechest.png, other_op.png</em>), cardiomegaly example (<em>cardiomegaly_cc0.png</em>).</li> <li>Array data: Array data for the Intro to Medical Imaging lesson. Numpy arrays were created by processing and manipulation of publicly available data i.e. from <a href="https://doi.org/10.1109/TNS.1974.6499235">the Schepp Logan phantom</a> and from the <a href="https://fastmri.med.nyu.edu/">NYU FastMRI dataset</a></li> <li>Data for the anonymization exercises: ultrasound (<em>identifiable_us.jpg</em>) dowloaded from <a href="https://www.flickr.com/photos/jcarter/2461223727">here</a>, and DICOM data (<em>our_sample_dicom.dcm</em>) shared for this course specifically by a colleague</li> <li>Histopathology data: histopathology slide images from <a href="https://openslide.org/">openslide</a> library samples in the freely distributable test data </li> </ol> <p>These files represent various medical imaging modalities and formats commonly used in clinical research and practice. They are intended for educational purposes, allowing students to practice image processing techniques, machine learning applications, and statistical analysis of medical images using Python libraries such as scikit-image, pydicom, and SimpleITK.</p>
Medical Equipment Image Data set
<p>This data set aims to realise the medical equipment recognition to aid visual search through three deep learning models. The data set contains ten medical equipment classes: commodes, wheelchairs, walking frames, blood pressure monitors, breast pumps, thermometers, rippled mattresses, oximeters, crutches, and therapeutic ultrasound machines. We collected from online resources around 220 images for each medical equipment class. Each image class in the test set has around 40 images.</p> <p>Refer to the paper <a href="http://doi.org/10.3233/JIFS-212786">here</a> or in research gate (preprint).</p>
RIGA+ Dataset for Unsupervised Domain Adaptation in Medical Image Segmentation
<p>Different from the previous combined multi-domain dataset for unsupervised domain adaptation (UDA) in medical image segmentation, this multi-domain fundus image dataset contains annotations made by the same group of ophthalmologists. Hence the annotator bias among different datasets can be mitigated. Therefore, this dataset can provide a relatively fair benchmark for evaluating UDA methods in fundus image segmentation.</p> <p>This dataset is based on the RIGA[1] dataset and MESSIDOR[2] dataset. We appreciate their efforts devoted by the authors of [1] and [2].</p> <p>The six duplicated cases in the RIGA dataset are filtered out according to the <a href="https://www.adcis.net/en/third-party/messidor/">Errata</a>. We also remove the duplicated cases that exist in both the RIGA dataset and the MESSIDOR dataset by hash value matching.</p> <table align="center"> <caption>Details of the RIGA+ dataset</caption> <thead> <tr> <th scope="row">Domain</th> <th scope="col">Dataset</th> <th scope="col"> <p>Labeled Samples</p> <p>(Train+Test)</p> </th> <th scope="col"> <p>Unlabeled</p> <p>Samples</p> </th> </tr> </thead> <tbody> <tr> <th scope="row">Source</th> <td>BinRushed</td> <td>195 (195+0)</td> <td>0</td> </tr> <tr> <th scope="row">Source</th> <td>Magrabia</td> <td>95 (95+0)</td> <td>0</td> </tr> <tr> <th scope="row">Target</th> <td>MESSIDOR-BASE1</td> <td>173 (138+35)</td> <td>227</td> </tr> <tr> <th scope="row">Target</th> <td>MESSIDOR-BASE2</td> <td>148 (118+30)</td> <td>238</td> </tr> <tr> <th scope="row">Target</th> <td>MESSIDOR-BASE3</td> <td>133 (106+27)</td> <td>252</td> </tr> </tbody> </table> <p>[1] Almazroa A, Alodhayb S, Osman E, et al. Retinal fundus images for glaucoma analysis: the RIGA dataset[C]//Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. International Society for Optics and Photonics, 2018, 10579: 105790B.</p> <p>[2] Decencière E, Zhang X, Cazuguel G, et al. Feedback on a publicly distributed image database: the Messidor database[J]. Image Analysis & Stereology, 2014, 33(3): 231-234.</p> <p>If you find this dataset useful for your research, please consider citing the paper as follows:</p> <pre><code>@inproceedings{hu2022domain, title={Domain Specific Convolution and High Frequency Reconstruction based Unsupervised Domain Adaptation for Medical Image Segmentation}, author={Shishuai Hu and Zehui Liao and Yong Xia}, booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention}, year={2022}, organization={Springer} }</code></pre> <p> </p>
Radiomics in medical imaging: pitfalls and challenges in clinical management
<p>We uploaded theimages of the manuscript "Radiomics in medical imaging: pitfalls and challenges in clinical management"</p>
Chest X-Ray Image Dataset: A Resource for Medical Diagnosis and Machine Learning
<p>The Chest X-Ray Image Dataset is an extensive collection designed to support medical research and the development of diagnostic tools for COVID-19 detection. It consists of two distinct classes: COVID-19 affected X-ray images and normal X-ray images of the chest area, each covering the full lungs. This dataset provides a diverse range of X-ray images, capturing the unique characteristics of both healthy and COVID-19 affected lungs, making it an invaluable resource for training and testing machine learning models in medical image classification and analysis.</p>
MedSegBench: A Comprehensive Benchmark for Medical Image Segmentation in Diverse Data Modalities
<p>We split the dataset into two parts due to the maximum uploaded file limit. You can find other data in Version 1.</p> <p>This dataset is an article study and is under evaluation.</p> <p><code>You can access the article on <a href="https://www.nature.com/articles/s41597-024-04159-2">Nature</a>.</code></p> <p><code>Trained model weights and detailed prediction results for each dataset (<a href="../records/13381081" target="_blank" rel="noopener">Zenodo</a>)</code></p>
The Study About Mechanism of Transcranial Magnetic Stimulation Treatment of Depression Using Medical Imaging
ClinicalTrials.gov study NCT03500029. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Medical Imaging Decision And Support
ClinicalTrials.gov study NCT05490290. IPD Sharing: NO. Countries: 1. Publications: 3.
Study of the Effects of Dopaminergic Medications on Dopamine Transporter Imaging in Parkinson's Disease
ClinicalTrials.gov study NCT00096720. IPD Sharing: Not stated. Countries: 1. Publications: 17.
Magnetic Resonance Imaging of the Whole Body, Including Diffusion, in the Medical Evaluation of Breast Cancers at High Risk for Metastasis and the Follow-up of Metastatic Cancers
ClinicalTrials.gov study NCT02966574. IPD Sharing: NO. Countries: 1. Publications: 6.
Application of Medical Imaging Procedures in Surgery Implanting in Cancerology With the Aim to Reduce Invasive Acts
ClinicalTrials.gov study NCT00925509. IPD Sharing: Not stated. Countries: 1. Publications: 38.
Medical Images Collection Research
ClinicalTrials.gov study NCT06354829. IPD Sharing: NO. Countries: 1. Publications: 4.
The Inflammatory Process and the Medical Imaging in Patients With an Inflammatory Disease of the Central Nervous System.
ClinicalTrials.gov study NCT01567553. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Computational Medical Imaging and Prediction of Diffusion/FLAIR Mismatch in Stroke Patients
ClinicalTrials.gov study NCT05192161. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Image-guided Pleural Biopsy or Medical Thoracosocopy in Diagnosis of Pleural Disease
ClinicalTrials.gov study NCT05428891. IPD Sharing: NO. Countries: 1. Publications: 1.
Medical Imaging and Thermal Treatment for Breast Tumors Using Harmonic Motion Imaging (HMI)
ClinicalTrials.gov study NCT05219695. IPD Sharing: NO. Countries: 1. Publications: 2.
Prognostic and Diagnostic Added Value of Medical Imaging in Gynecological Cancer (PRODIGYN)
ClinicalTrials.gov study NCT05855941. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
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.