Generalisable deep learning method for mammographic density prediction across imaging techniques and self-reported race.

Background: Breast density is an important risk factor for breast cancer complemented by a higher risk of cancers being missed during screening of dense breasts due to reduced sensitivity of mammography. Automated, deep learning-based prediction of breast density could provide subject-specific risk assessment and flag difficult cases during screening. However, there is a lack of evidence for generalisability across imaging techniques and, importantly, across race.

Automatic correction of performance drift under acquisition shift in medical image classification.

Image-based prediction models for disease detection are sensitive to changes in data acquisition such as the replacement of scanner hardware or updates to the image processing software. The resulting differences in image characteristics may lead to drifts in clinically relevant performance metrics which could cause harm in clinical decision making, even for models that generalise in terms of area under the receiver-operating characteristic curve. We propose Unsupervised Prediction Alignment, a generic automatic recalibration method that requires no ground truth annotations and only limited amounts of unlabelled example images from the shifted data distribution.

Multi-vendor evaluation of artificial intelligence as an independent reader for double reading in breast cancer screening on 275,900 mammograms.

Background: Double reading (DR) in screening mammography increases cancer detection and lowers recall rates, but has sustainability challenges due to workforce shortages. Artificial intelligence (AI) as an independent reader (IR) in DR may provide a cost-effective solution with the potential to improve screening performance. Evidence for AI to generalise across different patient populations, screening programmes and equipment vendors, however, is still lacking.

Dislocation loop formation by swift heavy ion irradiation of metals.

A coupled two-temperature, molecular dynamics methodology is used to simulate the structural evolution of bcc metals (Fe and W) and fcc metals (Cu and Ni) following irradiation by swift heavy ions. Electronic temperature dependent electronic specific heat capacities and electron-phonon coupling strengths are used to capture the full effects of the variation in the electronic density of states. Tungsten is found to be significantly more resistant to damage than iron, due both to the higher melting temperature and the higher thermal conductivity.

The influence of the electronic specific heat on swift heavy ion irradiation simulations of silicon.

The swift heavy ion (SHI) irradiation of materials is often modelled using the two-temperature model. While the model has been successful in describing SHI damage in metals, it fails to account for the presence of a bandgap in semiconductors and insulators. Here we explore the potential to overcome this limitation by explicitly incorporating the influence of the bandgap in the parameterisation of the electronic specific heat for Si.