medical image detection

GRAZPEDWRI-DX is a public dataset of 20,327 pediatric wrist trauma X-ray images released by the University of Medicine of Graz. These X-ray images were collected by multiple pediatric radiologists at the Department for Pediatric Surgery of the University Hospital Graz between 2008 and 2018, involving 6,091 patients and a total of 10,643 studies. This dataset is annotated with 74,459 image labels, featuring a total of 67,771 labeled objects.

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.

Heel Bone X-Ray Dataset consists of 3,956 X-ray images of the foot, primarily focused on detecting and classifying heel bone diseases. The images were obtained from Kirkuk General Hospital in Digital Imaging and Communications in Medicine (DICOM) format and converted to JPG format using the MicroDicom tool. The dataset is labeled into three distinct classes: Normal: 1,842 images Heel Spur: 1,316 images Severe (Heel Spur Complications): 798 images All labels were assigned by a specialist in orthopedics and traumatology and were cross-verified by a radiology specialist for validation, ensuring high-quality annotations. This dataset is valuable for training models on medical image classification, specifically targeting foot-related conditions. Dataset Size: 3,956 images Image Format: JPG (originally in DICOM format) Source: Kirkuk General Hospital https://www.kaggle.com/datasets/osamahtaher/heel-dataset/data Paper: HeCapsNet: An enhanced capsule network for automated heel disease diagnosis using lateral foot X-Ray images. Taher, Osamah, and Kasım Özacar. "HeCapsNet: An enhanced capsule network for automated heel disease diagnosis using lateral foot X‐Ray images." International Journal of Imaging Systems and Technology 34.3 (2024): e23084.

The thickness and appearance of retinal layers are essential markers for diagnosing and studying eye diseases. Despite the increasing availability of imaging devices to scan and store large amounts of data, analyzing retinal images and generating trial endpoints has remained a manual, error-prone, and time-consuming task. In particular, the lack of large amounts of high-quality labels for different diseases hinders the development of automated algorithms. Therefore, we have compiled 5016 pixel-wise manual labels for 1672 optical coherence tomography (OCT) scans featuring two different diseases as well as healthy subjects to help democratize the process of developing novel automatic techniques. We also collected 4698 bounding box annotations for a subset of 566 scans across 9 classes of disease biomarker. Due to variations in retinal morphology, intensity range, and changes in contrast and brightness, designing segmentation and detection methods that can generalize to different disease types is challenging. While machine learning-based methods can overcome these challenges, high-quality expert annotations are necessary for training. Publicly available annotated image datasets typically contain few images and/or only cover a single type of disease, and most are only annotated by a single grader. To address this gap, we present a comprehensive multi-grader and multi-disease dataset fortraining machine learning-based algorithms. The proposed dataset covers three subsets of scans (Age-related Macular Degeneration, Diabetic Macular Edema, and healthy) and annotations for two types of tasks (semantic segmentation and object detection).