Data Fusion Approach for Constructing Unsupervised Augmented VoxelBased Statistical Anthropomorphic Phantoms Approaches to constructing statistical phantoms (SPs) for the needs of data fusion with the real data have attracted a lot of attention in the recent years. SPs assist with radiation dosimetry and provide metrics for estimating image quality in the medical Xray systems. Unlike existing SPs, we propose to consider both material electron densities and anatomical size variations models to portray realistic variations in radiologic data. We introduce a new adaptive unsupervised fusion approach for data augmentation, capable of generating a variety of medically adequate images. The model is based on a combination of continuous Poisson modelling of voxel values, Monte Carlo rejection sampling scheme, and a landmarkbased warping. Unlike mere average of HU values of each organ (typical in the other stateofart SPs), our augmented voxels depict intensity fluctuations, effectively mimicking a distribution of electron densities within each organ. In the experimental section, we evaluate the proposed method and demonstrate its superiority compared to the existing methods. This phantom generation could be instrumental for assessing dose uncertainty, unsupervised refinement of image reconstruction, image classification, and semantic segmentation tasks carried out by machine learning algorithms in the scenarios of limited available data.

Multitask and Multimodal Neural Network Model for Interpretable Analysis of Xray Images The quality and interpretability of the stateoftheart methods for automatic analysis of chest Xray images is still not sufficient. We address this problem by presenting a model that combines the analysis of frontal chest Xray scans with structured patient information contained within radiology records. The proposed model generates a short textual summary with essential information on the found pathologies along with their location and severity; and the 2D heatmaps localizing each pathology on the original Xray images. We test the proposed model on the MIMICCXR dataset. It achieves the stateoftheart performance for image labelling and captioning (78.5% of correctly generated sentences) and defeats other similar solutions that dismiss the additional patient data (by 5.2% of correctly generated sentences). We also propose an automatic approach to label mining that leverages multimodal data: the Xray images, related textual reports, patientsu0027 age and sex.