End-to-End Fovea Localisation in Colour Fundus Images With a Hierarchical Deep Regression Network.

Accurately locating the fovea is a prerequisite for developing computer aided diagnosis (CAD) of retinal diseases. In colour fundus images of the retina, the fovea is a fuzzy region lacking prominent visual features and this makes it difficult to directly locate the fovea. While traditional methods rely on explicitly extracting image features from the surrounding structures such as the optic disc and various vessels to infer the position of the fovea, deep learning based regression technique can implicitly model the relation between the fovea and other nearby anatomical structures to determine the location of the fovea in an end-to-end fashion.

Attention by Selection: A Deep Selective Attention Approach to Breast Cancer Classification.

Deep learning approaches are widely applied to histopathological image analysis due to the impressive levels of performance achieved. However, when dealing with high-resolution histopathological images, utilizing the original image as input to the deep learning model is computationally expensive, while resizing the original image to achieve low resolution incurs information loss. Some hard-attention based approaches have emerged to select possible lesion regions from images to avoid processing the original image.

An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA.

One of the methods for stratifying different molecular classes of breast cancer is the Nottingham prognostic index plus, which uses breast cancer relevant biomarkers to stain tumor tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumor, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time-consuming, imprecise, and subjective process, which will lead to inter-observer and intra-observer discrepancies.

Positive mood on the day of influenza vaccination predicts vaccine effectiveness: A prospective observational cohort study.

Influenza vaccination is estimated to only be effective in 17-53% of older adults. Multiple patient behaviors and psychological factors have been shown to act as 'immune modulators' sufficient to influence vaccination outcomes. However, the relative importance of such factors is unknown as they have typically been examined in isolation.

A signalome screening approach in the autoinflammatory disease TNF receptor associated periodic syndrome (TRAPS) highlights the anti-inflammatory properties of drugs for repurposing.

TNF receptor associated periodic syndrome (TRAPS) is an autoinflammatory disease caused by mutations in TNF Receptor 1 (TNFR1). Current therapies for TRAPS are limited and do not target the pro-inflammatory signalling pathways that are central to the disease mechanism. Our aim was to identify drugs for repurposing as anti-inflammatories based on their ability to down-regulate molecules associated with inflammatory signalling pathways that are activated in TRAPS.