Find research datasets worth reusing

Checklist for AI in Medical Imaging (CLAIM) Consensus Panel

ClinicalTrials.gov study NCT05984082. IPD Sharing: Not stated. Countries: 1. Publications: 1.

The Construction and Effect Verification of a Deep Learning-based Automated Semantic Segmentation Model for Medical Imaging

ClinicalTrials.gov study NCT06864702. IPD Sharing: UNDECIDED. Countries: 1. Publications: 11.

A Post-market Clinical Evaluation of St. Jude Medical™ MR Conditional ICD System on Patients Undergoing Magnetic Resonance Imaging

ClinicalTrials.gov study NCT02877693. IPD Sharing: UNDECIDED. Countries: 3. Publications: 0.

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.

Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care: Study 2

ClinicalTrials.gov study NCT06646133. IPD Sharing: YES. Countries: 1. Publications: 6.

opencc-zeroDec 2018View details →

dryad28/100

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.

zenodo28/100

Hybrid Intelligence in Medical Image Segmentation

Title of publication: Hybrid Intelligence in Medical Image Segmentation <div>The original dataset contains x-rays and corresponding masks. Some masks are missing so it is advised to cross-reference the images and masks. The dataset link consisting of train and test is attached here,  </div> <div><a title="Original URL: https://www.kaggle.com/datasets/nikhilpandey360/chest-xray-masks-and-labels/data. Click or tap if you trust this link." href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.kaggle.com%2Fdatasets%2Fnikhilpandey360%2Fchest-xray-masks-and-labels%2Fdata&data=05%7C02%7CS.Oyelere%40exeter.ac.uk%7C80fa490cf7be41cf7a6508dd0069c919%7C912a5d77fb984eeeaf321334d8f04a53%7C0%7C0%7C638667176577007815%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=Fai5kYk%2FhaVfJp%2FhtFOFQfYSqcoFcpDTtiq%2FWiktUYY%3D&reserved=0" target="_blank" rel="noopener noreferrer">https://www.kaggle.com/datasets/nikhilpandey360/chest-xray-masks-and-labels/data</a></div> <div> </div> <div>The OP had the following request: It is requested that publications resulting from the use of this data attribute the source (National Library of Medicine, National Institutes of Health, Bethesda, MD, USA and Shenzhen No.3 People’s Hospital, Guangdong Medical College, Shenzhen, China) and cite the following publications: Jaeger S, Karargyris A, Candemir S, Folio L, Siegelman J, Callaghan F, Xue Z, Palaniappan K, Singh RK, Antani S, Thoma G, Wang YX, Lu PX, McDonald CJ. Automatic tuberculosis screening using chest radiographs. IEEE Trans Med Imaging. 2014 Feb;33(2):233-45. doi: 10.1109/TMI.2013.2284099. PMID: 24108713 Candemir S, Jaeger S, Palaniappan K, Musco JP, Singh RK, Xue Z, Karargyris A, Antani S, Thoma G, McDonald CJ. Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans Med Imaging. 2014 Feb;33(2):577-90. doi: 10.1109/TMI.2013.2290491. PMID: 24239990</div> <div> </div> <div>The dataset is split into test and validation also so the final ones are attached as : train data, test data link and  </div> <div>validation data link : The clinician masks are attached as: validation mask and testing mask.</div>

opencc-by-4.0Nov 2024View details →

A Clinical Study to Investigate the Effect of an Investigational Drug as an Added Medication to an Antipsychotic, in Adults With Schizophrenia, as Measured Positron Emission Tomography (PET) Imaging

ClinicalTrials.gov study NCT04038957. IPD Sharing: YES. Countries: 1. Publications: 0.

Brain Imaging: Cocaine Effects & Medication Development - 5

ClinicalTrials.gov study NCT00000270. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Imaging Study of Chronic Low Back Pain in Patients Taking Pain Medication

ClinicalTrials.gov study NCT00388414. IPD Sharing: Not stated. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →

Imaging the Neurobiology of Behavioral and Medication Treatment for Cocaine Dependence

ClinicalTrials.gov study NCT01468012. IPD Sharing: NO. Countries: 1. Publications: 0.

Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care. Study 1

ClinicalTrials.gov study NCT06646120. IPD Sharing: YES. Countries: 0. Publications: 5.

Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care. Study 3

ClinicalTrials.gov study NCT06645548. IPD Sharing: YES. Countries: 0. Publications: 6.

publicFeb 2019View details →

dryad28/100

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.

zenodo24/100

MedIMeta: A comprehensive and easy-to-use multi-domain multi-task medical imaging meta-dataset

We introduce the Medical Imaging Meta-Dataset (MedIMeta), a novel multi-domain, multi-task meta-dataset designed to facilitate the development and standardised evaluation of ML models and cross-domain few-shot learning algorithms for medical image classification. MedIMeta contains 19 medical imaging datasets spanning 10 different domains and encompassing 54 distinct medical tasks, offering opportunities for both single-task and multi-task training. All tasks are standardised to the same format and readily usable in PyTorch or other ML frameworks. All datasets have been previously published with an open license that allows redistribution or we obtained an explicit permission to do so. Each dataset within the MedIMeta dataset is standardized to a size of 224 × 224 pixels which matches image size commonly used in pre-trained models. Furthermore, the dataset comes with pre-made splits to ensure ease of use and standardized benchmarking. We release a user-friendly Python package to directly load images for use in PyTorch. <h3>Links</h3> <ul> <li>Project website: <a href="https://www.woerner.eu/projects/medimeta/" target="_blank" rel="noopener">https://www.woerner.eu/projects/medimeta/</a></li> <li>Data loading code (medimeta Python package): <a href="https://github.com/StefanoWoerner/medimeta-pytorch" target="_blank" rel="noopener">https://github.com/StefanoWoerner/medimeta-pytorch</a></li> <li>Data creation code: <a href="https://github.com/StefanoWoerner/medimeta-dataset-scripts" target="_blank" rel="noopener">https://github.com/StefanoWoerner/medimeta-dataset-scripts</a></li> </ul>   <h3>Dataset Overview</h3> <table> <tbody> <tr> <td>Dataset Name</td> <td>Dataset ID</td> <td>License</td> <td>Domain</td> <td>Task Names</td> <td>Task Targets</td> <td># Labels</td> </tr> <tr> <td>AML Cytomorphology</td> <td>aml</td> <td>CC BY-SA 4.0</td> <td>Microscopy</td> <td>morphological class</td> <td>multi-class classification</td> <td>15</td> </tr> <tr> <td>Breast Ultrasound</td> <td>bus</td> <td>CC BY-SA 4.0</td> <td>Breast ultrasound</td> <td>case category malignancy</td> <td>multi-class classification binary classification</td> <td>3 2</td> </tr> <tr> <td>Colorectal Cancer Histopathology</td> <td>crc</td> <td>CC BY-SA 4.0</td> <td>Histopathology</td> <td>tissue class</td> <td>multi-class classification</td> <td>9</td> </tr> <tr> <td>Chest X-ray Multi-disease</td> <td>cxr</td> <td>CC BY-SA 4.0</td> <td>Chest X-ray</td> <td>disease labels patient sex</td> <td>multi-label classification binary classification</td> <td>14 2</td> </tr> <tr> <td>Dermatoscopy</td> <td>derm</td> <td>CC BY-SA 4.0</td> <td>Dermatoscopy</td> <td>disease category</td> <td>multi-class classification</td> <td>7</td> </tr> <tr> <td>Diabetic Retinopathy (Regular Fundus)</td> <td>dr_regular</td> <td>CC BY-SA 4.0</td> <td>Retinal fundus</td> <td>DR level Overall quality Artifact Clarity Field definition</td> <td>ordinal regression binary classification ordinal regression ordinal regression ordinal regression</td> <td>5 2 6 5 5</td> </tr> <tr> <td>Diabetic Retinopathy (Ultra-widefield Fundus)</td> <td>dr_uwf</td> <td>CC BY-SA 4.0</td> <td>Retinal fundus</td> <td>DR level</td> <td>ordinal regression</td> <td>5</td> </tr> <tr> <td>Fundus Multi-disease</td> <td>fundus</td> <td>CC BY-SA 4.0</td> <td>Retinal fundus</td> <td>disease presence disease labels</td> <td>binary classification multi-label classification</td> <td>2 45</td> </tr> <tr> <td>Glaucoma-specific fundus images</td> <td>glaucoma</td> <td>CC BY-SA 4.0</td> <td>Retinal fundus</td> <td>Glaucoma suspect</td> <td>binary classification</td> <td>2</td> </tr> <tr> <td>Mammography (Calcifications)</td> <td>mammo_calc</td> <td>CC BY-SA 4.0</td> <td>Mammography</td> <td>pathology calc type calc distribution</td> <td>binary classification multi-label classification multi-label classification</td> <td>2 14 5</td> </tr> <tr> <td>Mammography (Masses)</td> <td>mammo_mass</td> <td>CC BY-SA 4.0</td> <td>Mammography</td> <td>pathology mass shape mass margins</td> <td>binary classification multi-label classification multi-label classification</td> <td>2 8 5</td> </tr> <tr> <td>OCT</td> <td>oct</td> <td>CC BY-SA 4.0</td> <td>OCT</td> <td>disease class urgent referral</td> <td>multi-class classification binary classification</td> <td>4 2</td> </tr> <tr> <td>Axial Organ Slices</td> <td>organs_axial</td> <td>CC BY-NC-SA 4.0</td> <td>Abdominal CT</td> <td>organ label</td> <td>multi-class classification</td> <td>11</td> </tr> <tr> <td>Coronal Organ Slices</td> <td>organs_coronal</td> <td>CC BY-NC-SA 4.0</td> <td>Abdominal CT</td> <td>organ label</td> <td>multi-class classification</td> <td>11</td> </tr> <tr> <td>Sagittal Organ Slices</td> <td>organs_sagittal</td> <td>CC BY-NC-SA 4.0</td> <td>Abdominal CT</td> <td>organ label</td> <td>multi-class classification</td> <td>11</td> </tr> <tr> <td>Peripheral Blood Cells</td> <td>pbc</td> <td>CC BY-SA 4.0</td> <td>Microscopy</td> <td>cell class</td> <td>multi-class classification</td> <td>8</td> </tr> <tr> <td>Pediatric Pneumonia</td> <td>pneumonia</td> <td>CC BY-SA 4.0</td> <td>Chest X-ray</td> <td>pneumonia presence disease class</td> <td>binary classification multi-class classification</td> <td>2 3</td> </tr> <tr> <td>Skin Lesion Evaluation (Dermoscopy)</td> <td>skinl_derm</td> <td>CC BY-SA 4.0</td> <td>Dermatoscopy</td> <td>Diagnosis Diagnosis grouped Pigment Network Blue Whitish Veil Vascular Structures Vascular Structures grouped Pigmentation Pigmentation grouped Streaks Dots and Globules Regression Structures Regression Structures grouped</td> <td>multi-class classification multi-class classification multi-class classification binary classification multi-class classification multi-class classification multi-class classification multi-class classification multi-class classification multi-class classification multi-class classification binary classification</td> <td>15 5 3 2 8 3 5 3 3 3 4 2</td> </tr> <tr> <td>Skin Lesion Evaluation (Clinical Photography)</td> <td>skinl_photo</td> <td>CC BY-SA 4.0</td> <td>Clinical skin imaging</td> <td>Diagnosis Diagnosis grouped Pigment Network Blue Whitish Veil Vascular Structures Vascular Structures grouped Pigmentation Pigmentation grouped Streaks Dots and Globules Regression Structures Regression Structures grouped</td> <td>multi-class classification multi-class classification multi-class classification binary classification multi-class classification multi-class classification multi-class classification multi-class classification multi-class classification multi-class classification multi-class classification binary classification</td> <td>15 5 3 2 8 3 5 3 3 3 4 2</td> </tr> </tbody> </table>

openApr 2024View details →

zenodo24/100

Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation (Unlabeled Data Part III)

Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. The paper can be found at https://arxiv.org/pdf/2206.08023.pdf In addition to providing the labeled 600 CT and MRI scans, we expect to provide 2000 CT and 1200 MRI scans without labels to support more learning tasks (semi-supervised, un-supervised, domain adaption, ...). The link can be found in: <ul> <li><a href="https://zenodo.org/deposit/7262581">labeled data (500CT+100MRI)</a></li> <li><a href="https://zenodo.org/record/7262757#.Y2iSQ9JBwYs">unlabeled data Part I (900CT)</a></li> <li><a href="https://zenodo.org/record/7295661#.Y2iR_9JBwYs">unlabeled data Part II (1100CT)</a> (Now there are 1000CT, we will replenish to 1100CT)</li> <li><a href="https://zenodo.org/record/7295816">unlabeled data Part III (1200MRI)</a></li> </ul> if you found this dataset useful for your research, please cite: <blockquote> <pre>@article{ji2022amos, title={AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation}, author={Ji, Yuanfeng and Bai, Haotian and Yang, Jie and Ge, Chongjian and Zhu, Ye and Zhang, Ruimao and Li, Zhen and Zhang, Lingyan and Ma, Wanling and Wan, Xiang and others}, journal={arXiv preprint arXiv:2206.08023}, year={2022} }</pre> </blockquote>

opencc-by-4.0Oct 2022View details →

zenodo24/100

Med-ReLU: A Hybrid Activation Function Tailored for Deep Artificial Neural Networks in Medical Image Segmentation without Parameter Tuning

Background: Deep learning (DL) is derived from the domain of Artificial Neural Network (ANN). It makes one of the most important elements of deep learning algorithms. Deep learning segmentation models are based on layer-by-layer convolution learning attribute representation directed by forward and backward propagation. Throughout the process vital role is played by appropriately chosen activation function (AF) in order to guarantee the robustness of the model learning. However, the existing activation functions are either ineffective in addressing the vanishing gradient problem or get burdened with multiple parameters that need to be manually tuned. Moreover, the current research on activation function design mainly focuses on classification tasks using natural images from the MNIST, CIFAR-10 and CIFAR-100 datasets. Therefore,Med-ReLU as a novel activation function for medical image segmentation, is proposed. The proposed activation function avoids deep learning models from the attacks of dead neurons or from the vanishing gradient problems. Method: Med-ReLU is a hybrid activation function that combines the property of two activation functions of ReLU and Softsign. For positive inputs, Med-ReLU utilizes the linear property just like ReLU to produce an output without vanishing gradient. The negative inputs converge in polynomial ways towards their asymptotes as property of the softsign AF that ensures robust training processing without the problem of dead neurons that rarely activate across the entire training dataset. Results: The training performance and segmentation accuracy of Med-ReLU have been investigated. The proposed function has demonstrated stable training and does not suffer from over-fitting. Hence, Med-ReLU has consistently outperformed the existing state-of-art activation functions in medical image segmentation tasks. Conclusion: Med-ReLU has been designed as a parameter-free activation function for DL image segmentation tasks. This activation function is easy-to-implement on complex and deep learning models. The utility of this research lies in affirming the impact of Med-ReLU on different Artificial Neural Network architectures and for various kinds of anomaly addressing tasks.

opencc-by-4.0Sep 2023View details →

ClinicalTrials.gov24/100

Role of rhPSMA-7.3 PET/CT Imaging in Men With High-Risk Prostate Cancer Following Conventional Imaging and Associated Changes in Medical Management

ClinicalTrials.gov study NCT05799248. IPD Sharing: Not stated. Countries: 1. Publications: 0.

ClinicalTrials.gov24/100

Determination of the Aneurysm Vulnerability Index by Stimulation and Medical Imaging

ClinicalTrials.gov study NCT05189041. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.