Fair evaluation of federated learning algorithms for automated breast density classification: The results of the 2022 ACR-NCI-NVIDIA federated learning challenge.

The correct interpretation of breast density is important in the assessment of breast cancer risk. AI has been shown capable of accurately predicting breast density, however, due to the differences in imaging characteristics across mammography systems, models built using data from one system do not generalize well to other systems. Though federated learning (FL) has emerged as a way to improve the generalizability of AI without the need to share data, the best way to preserve features from all training data during FL is an active area of research.

TRODO: A public vehicle odometers dataset for computer vision.

In the field of transportation and logistics, smart vision systems have been employed successfully to automate various tasks such as number-plate recognition and vehicle identity recognition. The development of such automated systems is possible with the availability of large image datasets having proper annotations. The TRODO dataset is a rich-annotated collection of odometer displays that can enable automatic mileage reading from raw images.