Emma Bluemke

TL;DR: This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems.

TL;DR: This chapter introduces Duet: the authors' tool for easier FL for scientists and data owners and provides a proof-of-concept demonstration of a FL workflow using an example of how to train a convolutional neural network.

TL;DR: A 3D CNN architecture to predict chronological age, using a training dataset of 12,802 T1-weighted MRI images and a further 6,885 images for testing, and it is shown that the use of nonlinearly registered images to train CNNs can lead to the network being driven by artefacts of the registration process and missing subtle indicators of ageing, limiting the clinical relevance.

TL;DR: The lipid MS profile of necrotic breast cancer with Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) imaging validated with statistical analysis and correlating pathology is reported.

TL;DR: Targeted and localized mass spectrometry imaging allows faster characterization of cancer compared to conventional methods and can be used for targeted and localized diagnosis of cancer.