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10 results for “Medical image analysis”
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
<p>This data repository for MedMNIST v1 is out of date! Please check the <a href="http://medmnist.github.io">latest version</a> of MedMNIST v2. </p> <p> </p> <p><strong>Abstract</strong></p> <p>We present MedMNIST, a collection of 10 pre-processed medical open datasets. MedMNIST is standardized to perform classification tasks on lightweight 28x28 images, which requires no background knowledge. Covering the primary data modalities in medical image analysis, it is diverse on data scale (from 100 to 100,000) and tasks (binary/multi-class, ordinal regression and multi-label). MedMNIST could be used for educational purpose, rapid prototyping, multi-modal machine learning or AutoML in medical image analysis. Moreover, MedMNIST Classification Decathlon is designed to benchmark AutoML algorithms on all 10 datasets; We have compared several baseline methods, including open-source or commercial AutoML tools. The datasets, evaluation code and baseline methods for MedMNIST are publicly available at <a href="https://medmnist.github.io/">https://medmnist.github.io/</a>.</p> <p> </p> <p>Please note that this dataset is <strong>NOT</strong> intended for clinical use.</p> <p> </p> <p>We recommend our official <a href="https://github.com/MedMNIST/MedMNIST">code</a> to download, parse and use the MedMNIST dataset:</p> <blockquote> <pre>pip install medmnist</pre> </blockquote> <p> </p> <p><strong>Citation and Licenses</strong></p> <p>If you find this project useful, please cite our ISBI'21 paper as:<br> <em> Jiancheng Yang, Rui Shi, Bingbing Ni. "MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis," arXiv preprint arXiv:2010.14925, 2020.</em><br> <br> or using bibtex:<br> <em> @article{medmnist,<br> title={MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis},<br> author={Yang, Jiancheng and Shi, Rui and Ni, Bingbing},<br> journal={arXiv preprint arXiv:2010.14925},<br> year={2020}<br> }</em></p> <p>Besides, please cite the corresponding paper if you use any subset of MedMNIST. Each subset uses the <strong>same license</strong> as that of the source dataset.</p> <p> </p> <p><strong>PathMNIST</strong></p> <p>Jakob Nikolas Kather, Johannes Krisam, et al., "Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study," PLOS Medicine, vol. 16, no. 1, pp. 1–22, 01 2019.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p> <p> </p> <p><strong>ChestMNIST</strong></p> <p>Xiaosong Wang, Yifan Peng, et al., "Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases," in CVPR, 2017, pp. 3462–3471.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0 1.0</a></em></p> <p> </p> <p><strong>DermaMNIST</strong></p> <p>Philipp Tschandl, Cliff Rosendahl, and Harald Kittler, "The ham10000 dataset, a large collection of multisource dermatoscopic images of common pigmented skin lesions," Scientific data, vol. 5, pp. 180161, 2018.</p> <p>Noel Codella, Veronica Rotemberg, Philipp Tschandl, M. Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, Harald Kittler, and Allan Halpern: “Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)”, 2018; arXiv:1902.03368.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by-nc/4.0/">CC BY-NC 4.0</a></em></p> <p> </p> <p><strong>OCTMNIST/PneumoniaMNIST</strong></p> <p>Daniel S. Kermany, Michael Goldbaum, et al., "Identifying medical diagnoses and treatable diseases by image-based deep learning," Cell, vol. 172, no. 5, pp. 1122 – 1131.e9, 2018.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p> <p> </p> <p><strong>RetinaMNIST</strong></p> <p>DeepDR Diabetic Retinopathy Image Dataset (DeepDRiD), "The 2nd diabetic retinopathy – grading and image quality estimation challenge," https://isbi.deepdr.org/data.html, 2020.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p> <p> </p> <p><strong>BreastMNIST</strong></p> <p>Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled, and Aly Fahmy, "Dataset of breast ultrasound images," Data in Brief, vol. 28, pp. 104863, 2020.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p> <p> </p> <p><strong>OrganMNIST_{Axial,Coronal,Sagittal}</strong></p> <p>Patrick Bilic, Patrick Ferdinand Christ, et al., "The liver tumor segmentation benchmark (lits)," arXiv preprint arXiv:1901.04056, 2019.</p> <p>Xuanang Xu, Fugen Zhou, et al., "Efficient multiple organ localization in ct image using 3d region proposal network," IEEE Transactions on Medical Imaging, vol. 38, no. 8, pp. 1885–1898, 2019.</p> <p><em><strong>License</strong>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p> <div> <div class="gtx-trans-icon"> </div> </div>
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>
A Retrospective Analysis of Magnetic Resonance Imaging Data for Breast Cancer Screening in the Open Consortium for Decentralized Medical Artificial Intelligence
ClinicalTrials.gov study NCT05698056. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data from: An integrated iterative annotation technique for easing neural network training in medical image analysis
Neural networks promise to bring robust, quantitative analysis to medical fields. However, their adoption is limited by the technicalities of training these networks and the required volume and quality of human-generated annotations. To address this gap in the field of pathology, we have created an intuitive interface for data annotation and the display of neural network predictions within a commonly used digital pathology whole-slide viewer. This strategy used a 'human-in-the-loop' to reduce the annotation burden. We demonstrate that segmentation of human and mouse renal micro compartments is repeatedly improved when humans interact with automatically generated annotations throughout the training process. Finally, to show the adaptability of this technique to other medical imaging fields, we demonstrate its ability to iteratively segment human prostate glands from radiology imaging data.
Data from: An integrated iterative annotation technique for easing neural network training in medical image analysis
Open the record for dataset details and reuse information.
Prediction of the Response to Neoadjuvant Radiation Chemotherapy Through Texture Analysis Derived From Medical Imaging
ClinicalTrials.gov study NCT04920435. IPD Sharing: YES. Countries: 1. Publications: 0.
A Clinical Trial of the Effectiveness and Safety of Software Assisting Diagnose the Intestinal Polyp Digestive Endoscopy by Analysis of Colonoscopy Medical Images From Electronic Digestive Endoscopy E
ClinicalTrials.gov study NCT05687318. IPD Sharing: NO. Countries: 1. Publications: 0.
Experiment on the Use of Innovative Computer Vision Technologies for Analysis of Medical Images in the Moscow Healthcare System
ClinicalTrials.gov study NCT04489992. IPD Sharing: NO. Countries: 1. Publications: 0.
Precision Medical Diagnosis for Parkinson's Disease - The Quantitative Analysis System for PET/MRI Images in Patients
ClinicalTrials.gov study NCT03909828. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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