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ShareScore release 0.9.0
Dataset results
13 results for “Computer-Aided Diagnosis”
Figure 1 in Strategies for false positive reduction and multimodal lesion characterization in computer-aided diagnosis of breast cancer
Figure 1. - Representative ultrasound images at four-month post copulation (8 Sep. 2010) before resorption, five-month post copulation (20 Oct. 2010) during resorption, and six-month post copulation (3 Nov. 2010) after resorption. A: Uterine horn; B: Fetus; C: Ovary; D: Follicle.
BUS-BRA: A Breast Ultrasound Dataset for Assessing Computer-aided Diagnosis Systems
<p>The BUS-BRA Dataset is a publicly available dataset of anonymized breast ultrasound (BUS) images of 1,064 patients that underwent routinary breast studies. The BUS dataset contains biopsy-proven tumor cases and BI-RADS annotations in categories 2, 3, 4, and 5. In addition, the dataset also contains ground truth delineations that divide the BUS images into tumoral and normal regions. A full description of the BUS-BRA dataset can be found in the following article:</p> <p>Wilfrido Gómez-Flores, Maria Julia Gregorio-Calas, and Wagner Coelho de Albuquerque Pereira, "BUS-BRA: A Breast Ultrasound Dataset for Assessing Computer-aided Diagnosis Systems," Medical Physics, vol. 51, pp. 3110-3123, 2024, DOI: 10.1002/mp.16812.</p> <p>Any research originating from its usage is required to cite this paper.</p> <p>The Program of Biomedical Engineering of the Federal University of Rio de Janeiro (PEB/COPPE-UFRJ, Brazil) has a copyright on the data and is the principal distributor of the BUS-BRA Dataset. In addition, the Centro de Investigación y de Estudios Avanzados (Cinvestav, Mexico) is involved in an ongoing effort to develop this dataset to aid research efforts in developing, testing, and evaluating algorithms for breast ultrasound analysis.</p>
Development of Computer-aided Detection and Diagnosis From Imaging Techniques
ClinicalTrials.gov study NCT00057252. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Impact of Computer-aided Optical Diagnosis (CAD) in Predicting Histology of Diminutive Rectosigmoid Polyps: a Multicenter Prospective Trial (ABC Study).
ClinicalTrials.gov study NCT04607083. IPD Sharing: NO. Countries: 1. Publications: 1.
Assessment of a Radiomics-based Computer-Aided Diagnosis Tool for Pulmonary nodulES
ClinicalTrials.gov study NCT05968898. IPD Sharing: NO. Countries: 1. Publications: 7.
Data from: Predicting classifier performance with limited training data: applications to computer-aided diagnosis in breast and prostate cancer
Clinical trials increasingly employ medical imaging data in conjunction with supervised classifiers, where the latter require large amounts of training data to accurately model the system. Yet, a classifier selected at the start of the trial based on smaller and more accessible datasets may yield inaccurate and unstable classification performance. In this paper, we aim to address two common concerns in classifier selection for clinical trials: (1) predicting expected classifier performance for large datasets based on error rates calculated from smaller datasets and (2) the selection of appropriate classifiers based on expected performance for larger datasets. We present a framework for comparative evaluation of classifiers using only limited amounts of training data by using random repeated sampling (RRS) in conjunction with a cross-validation sampling strategy. Extrapolated error rates are subsequently validated via comparison with leave-one-out cross-validation performed on a larger dataset. The ability to predict error rates as dataset size increases is demonstrated on both synthetic data as well as three different computational imaging tasks: detecting cancerous image regions in prostate histopathology, differentiating high and low grade cancer in breast histopathology, and detecting cancerous metavoxels in prostate magnetic resonance spectroscopy. For each task, the relationships between 3 distinct classifiers (k-nearest neighbor, naive Bayes, Support Vector Machine) are explored. Further quantitative evaluation in terms of interquartile range (IQR) suggests that our approach consistently yields error rates with lower variability (mean IQRs of 0.0070, 0.0127, and 0.0140) than a traditional RRS approach (mean IQRs of 0.0297, 0.0779, and 0.305) that does not employ cross-validation sampling for all three datasets.
Data from: Predicting classifier performance with limited training data: applications to computer-aided diagnosis in breast and prostate cancer
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Evaluation of a Computer-aided Diagnosis System (CADx) in the Early Detection of Gastric Cancer in France. Cancer in France.
ClinicalTrials.gov study NCT05928819. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Computer-Aided Diagnosis for Hepatocellular Carcinoma Microvascular Invasion
ClinicalTrials.gov study NCT07170345. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Digital Mammography: Computer-Aided Breast Cancer Diagnosis
ClinicalTrials.gov study NCT00732433. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Artificial Intelligence-Based Computer-Aided Diagnosis of Prostate Cancer
ClinicalTrials.gov study NCT05513638. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Computer-aided Diagnosis of Ocular Diseases Based on Corneal Biomechanics
ClinicalTrials.gov study NCT04475900. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Prospective Study to Evaluate the Diagnostic Accuracy of Computer-aided Diagnosis (CADx) System in Real-time Characterization of Colorectal Neoplasia
ClinicalTrials.gov study NCT05414383. IPD Sharing: NO. Countries: 0. 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.