ChestX-ray14 is a medical imaging dataset which comprises 112,120 frontal-view X-ray images of 30,805 (collected from the year of 1992 to 2015) unique patients with the text-mined fourteen common disease labels, mined from the text radiological reports via NLP techniques. It expands on ChestX-ray8 by adding six additional thorax diseases: Edema, Emphysema, Fibrosis, Pleural Thickening and Hernia. Source: https://nihcc.app.box.com/v/ChestXray-NIHCC/file/220660789610 Image Source: https://nihcc.app.box.com/v/ChestXray-NIHCC
The goal of the Automated Cardiac Diagnosis Challenge (ACDC) challenge is to: - compare the performance of automatic methods on the segmentation of the left ventricular endocardium and epicardium as the right ventricular endocardium for both end diastolic and end systolic phase instances; - compare the performance of automatic methods for the classification of the examinations in five classes (normal case, heart failure with infarction, dilated cardiomyopathy, hypertrophic cardiomyopathy, abnormal right ventricle). The overall ACDC dataset was created from real clinical exams acquired at the University Hospital of Dijon. Acquired data were fully anonymized and handled within the regulations set by the local ethical committee of the Hospital of Dijon (France). Our dataset covers several well-defined pathologies with enough cases to (1) properly train machine learning methods and (2) clearly assess the variations of the main physiological parameters obtained from cine-MRI (in particular diastolic volume and ejection fraction). The dataset is composed of 150 exams (all from different patients) divided into 5 evenly distributed subgroups (4 pathological plus 1 healthy subject groups) as described below. Furthermore, each patient comes with the following additional information : weight, height, as well as the diastolic and systolic phase instants. The database is made available to participants through two datasets from the dedicated online evaluation website after a personal registration: i) a training dataset of 100 patients along with the corresponding manual references based on the analysis of one clinical expert; ii) a testing dataset composed of 50 new patients, without manual annotations but with the patient information given above. The raw input images are provided through the Nifti format. Source: Automated Cardiac Diagnosis Challenge Image source: Automated Cardiac Diagnosis Challenge
The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is conducted with immunohistochemical techniques (IHC), which is very expensive. Therefore, we propose a breast cancer immunohistochemical (BCI) benchmark attempting to synthesize IHC data directly with the paired hematoxylin and eosin (HE) stained images. The dataset contains 4870 registered image pairs, covering a variety of HER2 expression levels (0, 1+, 2+, 3+).
The normal chest X-ray (left panel) depicts clear lungs without any areas of abnormal opacification in the image. Bacterial pneumonia (middle) typically exhibits a focal lobar consolidation, in this case in the right upper lobe (white arrows), whereas viral pneumonia (right) manifests with a more diffuse ‘‘interstitial’’ pattern in both lungs. http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5
The Kvasir-VQA dataset is an extended dataset derived from the HyperKvasir and Kvasir-Instrument datasets, augmented with question-and-answer annotations. This dataset is designed to facilitate advanced machine learning tasks in gastrointestinal (GI) diagnostics, including image captioning, Visual Question Answering (VQA) and text-based generation of synthetic medical images.
This dataset includes sharp-blur pairs of Leishmania image, which is a protozoan parasite microscopy image dataset of Leishmania, obtained from the preserved slides stained with Giemsa. The paired blur-sharp images are acquired by employing a bright-field microscope (Olympus IX53) with 100× magnification oil immersion objectives.We first capture the sharp images as ground truth, then acquire its corresponding out-of-focus images. The extent and nature of defocusing are random along the optical axis, where the degree of out-of-focus is inconsistent from image-to-image. This dataset includes 764 in-focus and 764 corresponding out-of-focus images, where each image is composed of 2304 × 1728 pixels in 24-bit JPG format.