123 machine learning datasets
123 dataset results
the MTHS dataset contains 30Hz PPG signals obtained from 62 patients, including 35 men and 27 women. The ground truth data includes heart rate and oxygen saturation levels sampled at 1Hz. The HR and SPo2 measurement is obtained using a pulse oximeter (M70). An iPhone 5s was used to obtain the ppg recordings at 30 fps.
Several datasets are fostering innovation in higher-level functions for everyone, everywhere. By providing this repository, we hope to encourage the research community to focus on hard problems. In this repository, we present the real results severity (BIRADS) and pathology (post-report) classifications provided by the Radiologist Director from the Radiology Department of Hospital Fernando Fonseca while diagnosing several patients (see dataset-uta4-dicom) from our User Tests and Analysis 4 (UTA4) study. Here, we provide a dataset for the measurements of both severity (BIRADS) and pathology classifications concerning the patient diagnostic. Work and results are published on a top Human-Computer Interaction (HCI) conference named AVI 2020 (page). Results were analyzed and interpreted from our Statistical Analysis charts. The user tests were made in clinical institutions, where clinicians diagnose several patients for a Single-Modality vs Multi-Modality comparison. For example, in these t
Full-text chemical identification and indexing in PubMed articles.
The MIMIC PERform Testing dataset contains the following physiological signals recorded from 200 critically-ill patients during routine clinical care:
Abstract The Norwegian Endurance Athlete ECG Database contains 12-lead ECG recordings from 28 elite athletes from various sports in Norway. All recordings are 10 seconds resting ECGs recorded with a General Electric (GE) MAC VUE 360 electrocardiograph. All ECGs are interpreted with both the GE Marquette SL12 algorithm (version 23 (v243)) and one cardiologist with training in interpretation of athlete's ECG. The data was collected at the University of Oslo in February and March 2020.
Electrophysiological data from implanted electrodes in the human brain are rare, and therefore scientific access to it has remained somewhat exclusive. Here we present a freely-available curated library of implanted electrocorticographic (ECoG) data and analyses for 16 benchmark behavioral experiments, with 204 individual datasets from 34 patients made with the same amplifiers (at the same sampling rate and filter settings). In every case, electrode positions have been carefully registered to brain anatomy. A large set of fully-commented analysis scripts to interpret these data using modern techniques is embedded in the library alongside the data. All data, anatomic correlations, and analysis files (MATLAB code) are in a common, intuitive file structure at https://searchworks.stanford.edu/view/zk881ps0522. The library may be used as course material or serve as a starter package for researchers early in their career or for established groups, to modify the analyses and re-apply them in
Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge. Through FetReg2021 challenge, we released the first large-scale multi-centre dataset of fetoscopy laser photocoagulation procedure. The dataset contains 2,718 pixel-wise annotated images (for background, vessel, fetus, tool classes) from 24 different in vivo TTTS fetoscopic surgeries and 24 unannotated video clips video clips containing 9,616 frames for training and testing. The dataset is useful for the development of generalized and robust semantic segmentation and video mosaicking algorithms for long duration fetoscopy videos.
The PAX-Ray++ dataset uses pseudo-labeled thorax CTs to enable the segmentation of anatomy in Chest X-Rays. By projecting the CTs to a 2D plane, we gather fine-grained annotated imaages resembling radiographs. It contains 7,377 frontal and lateral view images each with 157 anatomy classes and over 2 million annotated instances.
To advance methods for pain assessment, in particular automatic assessment methods, the BioVid Heat Pain Database was collected in a collaboration of the Neuro-Information Technology group of the University of Magdeburg and the Medical Psychology group of the University of Ulm. In our study, 90 participants were subjected to experimentally induced heat pain in four intensities. To compensate for varying heat pain sensitivities, the stimulation temperatures were adjusted based on the subject-specific pain threshold and pain tolerance. Each of the four pain levels was stimulated 20 times in randomized order. For each stimulus, the maximum temperature was held for 4 seconds. The pauses between the stimuli were randomized between 8-12 seconds. The pain stimulation experiment was conducted twice: once with un-occluded face and once with facial EMG sensors.
Background: Lung cancer risk classification is an increasingly important area of research as low-dose thoracic CT screening programs have become standard of care for patients at high risk for lung cancer. There is limited availability of large, annotated public databases for the training and testing of algorithms for lung nodule classification.
MedMNIST-C is an open-source data set collection comprising algorithmically generated corruptions applied to the test sets of the MedMNIST collection following the concept of ImageNet-C. To maintain the integrity of the medical data, we have excluded any weather-dependent corruptions (“Snow”, “Frost”, “Fog”). Hence, each data set in the MedMNIST-C collection comprises 16 different corruptions (12 test corruptions and 4 validation corruptions) spanning 5 severity levels. For further information on the corruptions visit the original GitHub repository of ImageNet-C.
The LeukemiaAttri dataset is a large-scale, multi-domain collection of microscopy images derived from leukemia patient samples, enriched with detailed morphological information. This dataset comprises a total of 28.9K images (2.4K × 2 × 3 × 2), which were captured using both low-cost and high-cost microscopes at three different resolutions: 10x, 40x, and 100x, utilizing various cameras. In addition to providing location annotations for each white blood cell (WBC), the dataset includes comprehensive morphological attributes for every WBC, enhancing its utility for research and analysis in the field.
To take advantage of the ever-increasing amount of structural data now available, we also trained Paragraph on a larger dataset. This new dataset was extracted from the Structural Antibody Database (SAbDab, Schneider et al., 2022) on March 31, 2022 and includes 1086 complexes which we divide into train, validation and test sets using a 60-20-20 split. Full details of both datasets are given in the Supplementary Information.
a dataset of multi-modal signals from wearable devices at four sites on the body. Each device continuously recorded synchronized signals from a 3-channel reflective photoplethysmogram (red, green, infrared PPG), 3-axis inertial sensor (accelerometer), temperature, and barometric altitude sensor. For reference, the sternum device continuously recorded a Lead-I electrocardiogram (ECG) from body-mounted gel electrodes to provide ground-truth heart rate (HR) estimates.
A public open dataset of synthetic chest X-ray images of COVID-19.
Mouse Brain MRI atlas (both in-vivo and ex-vivo) (repository relocated from the original webpage)
SinGAN-Seg-polyps is a synthetic dataset for polyp segmentation consisting of 10,000 synthetic polyps and masks.
BioLeaflets is a biomedical dataset for Data2Text generation. It is a corpus of 1,336 package leaflets of medicines authorised in Europe, which were obtained by scraping the European Medicines Agency (EMA) website. Package leaflets are included in the packaging of medicinal products and contain information to help patients use the product safely and appropriately, under the guidance of their healthcare professional. Each document contains six sections: 1) What is the product and what is it used for 2) What you need to know before you take the product 3) product usage instructions 4) possible side effects, 5) product storage conditions 6) other information.
The BCSS dataset contains over 20,000 segmentation annotations of tissue regions from breast cancer images from The Cancer Genome Atlas (TCGA). This large-scale dataset was annotated through the collaborative effort of pathologists, pathology residents, and medical students using the Digital Slide Archive. It enables the generation of highly accurate machine-learning models for tissue segmentation.
This is the supplemental data for our paper on how to benchmark registrations of serial sections with ground truths. There are three main modalities and one further, as a reference.