Medical Image Analysis

Chaoyang dataset contains 1111 normal, 842 serrated, 1404 adenocarcinoma, 664 adenoma, and 705 normal, 321 serrated, 840 adenocarcinoma, 273 adenoma samples for training and testing, respectively. This noisy dataset is constructed in the real scenario. - Details: Colon slides from Chaoyang hospital, the patch size is 512 × 512. We invited 3 professional pathologists to label the patches, respectively. We took the parts of labeled patches with consensus results from 3 pathologists as the testing set. Others we used as the training set. For the samples with inconsistent labeling opinions of the three doctors in the training set (this part accounts for about 40%), we randomly selected the opinions from one of the three doctors. - The original WSIs are scanned at X20 objective magnification.

HyperKvasir dataset contains 110,079 images and 374 videos where it captures anatomical landmarks and pathological and normal findings. A total of around 1 million images and video frames altogether.

The Breast Cancer Histopathological Image Classification (BreakHis) is composed of 9,109 microscopic images of breast tumor tissue collected from 82 patients using different magnifying factors (40X, 100X, 200X, and 400X). It contains 2,480 benign and 5,429 malignant samples (700X460 pixels, 3-channel RGB, 8-bit depth in each channel, PNG format). This database has been built in collaboration with the P&D Laboratory - Pathological Anatomy and Cytopathology, Parana, Brazil. Paper: F. A. Spanhol, L. S. Oliveira, C. Petitjean and L. Heutte, "A Dataset for Breast Cancer Histopathological Image Classification," in IEEE Transactions on Biomedical Engineering, vol. 63, no. 7, pp. 1455-1462, July 2016, doi: 10.1109/TBME.2015.2496264 Source: https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/ Image Source: https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/

Autism spectrum disorder (ASD) is characterized by qualitative impairment in social reciprocity, and by repetitive, restricted, and stereotyped behaviors/interests. Previously considered rare, ASD is now recognized to occur in more than 1% of children. Despite continuing research advances, their pace and clinical impact have not kept up with the urgency to identify ways of determining the diagnosis at earlier ages, selecting optimal treatments, and predicting outcomes. For the most part this is due to the complexity and heterogeneity of ASD. To face these challenges, large-scale samples are essential, but single laboratories cannot obtain sufficiently large datasets to reveal the brain mechanisms underlying ASD. In response, the Autism Brain Imaging Data Exchange (ABIDE) initiative has aggregated functional and structural brain imaging data collected from laboratories around the world to accelerate our understanding of the neural bases of autism. With the ultimate goal of facilitating discovery science and comparisons across samples, the ABIDE initiative now includes two large-scale collections: ABIDE I and ABIDE II. Each collection was created through the aggregation of datasets independently collected across more than 24 international brain imaging laboratories and are being made available to investigators throughout the world, consistent with open science principles, such as those at the core of the International Neuroimaging Data-sharing Initiative. For details about these initiatives visit the collection specific pages: ABIDE I and ABIDE II.

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 our medical imaging DICOM files of patients from our User Tests and Analysis 4 (UTA4) study. Here, we provide a dataset of the used medical images during the UTA4 tasks. This repository and respective dataset should be paired with the dataset-uta4-rates repository dataset. Work and results are published on a top Human-Computer Interaction (HCI) conference named AVI 2020 (page). Results were analyzed and interpreted on 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 tests, we used both prototype-single-modality and prototype-multi-modality repositories for the comparison. On the same hand, the hereby dataset represents the pieces of information of both BreastScreening and MIDA projects. These projects are research projects that deal with the use of a recently proposed technique in literature: Deep Convolutional Neural Networks (CNNs). From a developed User Interface (UI) and framework, these deep networks will incorporate several datasets in different modes. For more information about the available datasets please follow the Datasets page on the Wiki of the meta information repository. Last but not least, you can find further information on the Wiki in this repository. We also have several demos to see in our YouTube Channel, please follow us.

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 our severity rates (BIRADS) of clinicians while diagnosing several patients from our User Tests and Analysis 4 (UTA4) study. Here, we provide a dataset for the measurements of severity rates (BIRADS) 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 tests, we used both prototype-single-modality and prototype-multi-modality repositories for the comparison. On the same hand, the hereby dataset represents the pieces of information of both BreastScreening and MIDA projects. These projects are research projects that deal with the use of a recently proposed technique in literature: Deep Convolutional Neural Networks (CNNs). From a developed User Interface (UI) and framework, these deep networks will incorporate several datasets in different modes. For more information about the available datasets please follow the Datasets page on the Wiki of the meta information repository. Last but not least, you can find further information on the Wiki in this repository. We also have several demos to see in our YouTube Channel, please follow us.

The dataset has 93 image stacks and their corresponding Extended Depth of Field (EDF) image acquired from cases with grades Nagative, LSIL or HSIL (The Bethesda System): - Negative: 16 - LSIL: 46 - HSIL: 31 The ground truth includes the grade labels for each frame and manually marked points inside cervical cells in each frame. There are in total 2705 manually marked points inside all frames: - Negative: 238 - LSIL: 1536 - HSIL: 931 Source: https://github.com/parham-ap/cytology_dataset

The dataset is composed of Hematoxylin and eosin (H&E) stained breast histology microscopy and whole-slide images. Challenge participants should evaluate the performance of their method on either/both sets of images.

The Liver-US dataset is a comprehensive collection of high-quality ultrasound images of the liver, including both normal and abnormal cases. This dataset is designed to facilitate research in medical image classification, with a focus on liver-related conditions. It includes a diverse range of ultrasound images acquired from multiple clinical settings, providing a robust foundation for developing and validating machine learning models in medical image analysis. Detailed Dataset Description Dataset Characteristics: The Liver-US dataset contains735 ultrasound images of the liver, including both normal and abnormal cases. The dataset includes a variety of liver conditions, such as Hepatocellular Carcinoma (HCC), liver cysts, hemangiomas, and other focal liver lesions. The images were acquired using state-of-the-art ultrasound equipment, ensuring high resolution and contrast for accurate diagnosis. Motivations and Content Summary: The primary motivation behind the creation of the Liver-US dataset is to provide a comprehensive and diverse resource for researchers working on medical image classification task related to the liver. The dataset was compiled in collaboration with leading medical institutions and includes images from a diverse patient population, ensuring representativeness and generalizability. The annotations were performed by experienced radiologists, adhering to strict quality control standards to ensure accuracy and reliability. Potential Use Cases: Research and Development: The dataset is ideal for researchers and developers working on machine learning models for medical image classification and feature extraction. Clinical Applications: It can be used to train and validate computer-aided diagnosis systems for early detection and diagnosis of liver conditions. Educational Purposes: The dataset serves as a valuable resource for training medical professionals and students in the interpretation of ultrasound images and the diagnosis of liver conditions. Benchmarking: It provides a standardized benchmark for comparing the performance of different algorithms and models in the field of medical image analysis.

Different types of cells play a vital role in the initiation, development, invasion, metastasis and therapeutic response of tumors of various organs. For example, (1) most carcinomas originate from epithelial cells, (2) spatial arrangement of tumor infiltrating Lymphocytes (TILs) is associated with clinical outcome in several cancers, including the ones of breast, prostate, and lung (Fridman et. al., Nature Reviews Cancer, 2012), and (3) tumor associated macrophages (TAMs) influence diverse processes such as angiogenesis, neoplastic cell mitogenesis, antigen presentation, matrix degradation, and cytotoxicity in various tumors (Ruffel and Coussens, Cancer Cell, 2015). Thus, accurate identification and segmentation of nuclei of multiple cell-types is important for AI enabled characterization of tumor and its microenvironment. In this challenge, participants will be provided with H&E stained tissue images of four organs with annotations of multiple cell-types including epithelial cells, lymphocytes, macrophages, and neutrophils. Participants will use the annotated dataset to develop computer vision algorithms to recognize these cell-types from the tissue images of unseen patients released in the testing set of the challenge. Additionally, all cell-types will not have equal number of annotated instances in the training dataset which will encourage participants to develop algorithms for learning from imbalanced classes in a few shot learning paradigm. H&E staining of human tissue sections is a routine and most common protocol used by pathologists to enhance the contrast of tissue sections for tumor assessment (grading, staging, etc.) at multiple microscopic resolutions. Hence, we will provide the annotated dataset of H&E stained digitized tissue images of several patients acquired at multiple hospitals using one of the most common 40x scanner magnification. The annotations will be done with the help of expert pathologists.

MP-IDB comprises four species of Malaria parasites: Falciparum, Malariae, Ovale, Vivax. For each species, there are four distinct stages of life, described in the filenames as follows: R: indicates the presence of 1 or more Ring stage parasites T: indicates the presence of 1 or more Trophozoite stage parasites S: indicates the presence of 1 or more Schizont stage parasites G: indicates the presence of 1 or more Gametocyte stage parasites. Each original image has its own ground-truth, provided by expert pathologists, while every image has been analyzed from left to right, therefore every filename indicates the presence of the parasites following this indication.