Omkar2118/datasets huggingface dataset:
CheXphoto

CheXphoto is a competition for x-ray interpretation based on a new dataset of naturally and synthetically perturbed chest x-rays hosted by Stanford and VinBrain.

Chest radiography is the most common imaging examination globally, and is critical for screening, diagnosis, and management of many life threatening diseases. Most chest x-ray algorithms have been developed and validated on digital x-rays, while the vast majority of developing regions use films. An appealing solution to scaled deployment is to leverage the ubiquity of smartphones for automated interpretation of film through cellphone photography. Automated interpretation of photos of chest x-rays at the same high-level of performance as with digital chest x-rays is challenging because photographs of x-rays introduce visual artifacts not commonly found in digital x-rays. To encourage high model performance for this application, we developed CheXphoto, a dataset of photos of chest x-rays and synthetic transformations designed to mimic the effects of photography.

With the launch of the CheXphoto competition, we are pleased to announce the release of an additional set of x-ray film images provided by VinBrain, a subsidiary of Vingroup. Please see Validation and Test Sets for details.

Introducing paper: CheXphoto: 10,000+ Photos and Transformations of Chest X-rays for Benchmarking Deep Learning Robustness
Yahoo S5
Full name: Yahoo S5 - A Labeled Anomaly Detection Dataset

Automatic anomaly detection is critical in today's world where the sheer volume of data makes it impossible to tag outliers manually. The goal of this dataset is to benchmark your anomaly detection algorithm. The dataset consists of real and synthetic time-series with tagged anomaly points. The dataset tests the detection accuracy of various anomaly-types including outliers and change-points. The synthetic dataset consists of time-series with varying trend, noise and seasonality. The real dataset consists of time-series representing the metrics of various Yahoo services.
InBreast
Full name: InBreast

Rationale and objectives: Computer-aided detection and diagnosis (CAD) systems have been developed in the past two decades to assist radiologists in the detection and diagnosis of lesions seen on breast imaging exams, thus providing a second opinion. Mammographic databases play an important role in the development of algorithms aiming at the detection and diagnosis of mammary lesions. However, available databases often do not take into consideration all the requirements needed for research and study purposes. This article aims to present and detail a new mammographic database.

Materials and methods: Images were acquired at a breast center located in a university hospital (Centro Hospitalar de S. João [CHSJ], Breast Centre, Porto) with the permission of the Portuguese National Committee of Data Protection and Hospital's Ethics Committee. MammoNovation Siemens full-field digital mammography, with a solid-state detector of amorphous selenium was used.

**Results: **The new database-INbreast-has a total of 115 cases (410 images) from which 90 cases are from women with both breasts affected (four images per case) and 25 cases are from mastectomy patients (two images per case). Several types of lesions (masses, calcifications, asymmetries, and distortions) were included. Accurate contours made by specialists are also provided in XML format.

Conclusion: The strengths of the actually presented database-INbreast-relies on the fact that it was built with full-field digital mammograms (in opposition to digitized mammograms), it presents a wide variability of cases, and is made publicly available together with precise annotations. We believe that this database can be a reference for future works centered or related to breast cancer imaging.

Introducing paper: INbreast: toward a full-field digital mammographic database