Sample-Size Determination Methodologies for Machine Learning in Medical Imaging Research: A Systematic Review.

Purpose: The required training sample size for a particular machine learning (ML) model applied to medical imaging data is often unknown. The purpose of this study was to provide a descriptive review of current sample-size determination methodologies in ML applied to medical imaging and to propose recommendations for future work in the field.

Methods: We conducted a systematic literature search of articles using Medline and Embase with keywords including "machine learning," "image," and "sample size.

-Same-Net: -Anonymity with Generative Deep Neural Networks for Face Deidentification.

Image and video data are today being shared between government entities and other relevant stakeholders on a regular basis and require careful handling of the personal information contained therein. A popular approach to ensure privacy protection in such data is the use of deidentification techniques, which aim at concealing the identity of individuals in the imagery while still preserving certain aspects of the data after deidentification. In this work, we propose a novel approach towards face deidentification, called -Same-Net, which combines recent Generative Neural Networks (GNNs) with the well-known -Anonymitymechanism and provides formal guarantees regarding privacy protection on a closed set of identities.