Explainable early detection of Alzheimer's disease using ROIs and an ensemble of 138 3D vision transformers.

Early detection and accurate diagnosis of brain morphological abnormalities are essential for the effective management and treatment of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Structural magnetic resonance imaging (MRI) is a powerful support tool to aid in disease diagnosis and prediction. In this research study, we present an innovative approach to predict Alzheimer's disease (AD) and mild cognitive impairment (MCI) using MRI data, which integrates regional interest (ROI)-based methodology and deep learning within a comprehensible framework.

Multi-Label Retinal Disease Classification Using Transformers.

Early detection of retinal diseases is one of the most important means of preventing partial or permanent blindness in patients. In this research, a novel multi-label classification system is proposed for the detection of multiple retinal diseases, using fundus images collected from a variety of sources. First, a new multi-label retinal disease dataset, the MuReD dataset, is constructed, using a number of publicly available datasets for fundus disease classification.

Realistic forward and inverse model mesh generation for rapid three-dimensional thoracic electrical impedance imaging.

One of the most promising clinical applications of Electrical Impedance Tomography (EIT) is real-time monitoring of lung function in ambulatory or ICU due to the rapid, non-invasive and non-ionizing nature of the measurements. However, to move this modality into routine clinical use will, as a minimum, require the development of realistic and computationally efficient forward and inverse meshes of the thorax and the lungs alongside mechanisms to extract quantitative information from the resulting reconstructed images. The latter will allow for reduction of artefacts and better localization of conductivity changes within the image domain.