Development and Evaluation of Advanced Image Analysis Techniques for Pediatric Deep Vein Thrombosis Imaging Scans Deep vein thrombosis (DVT) is a condition, which results from atherosclerosis and the possible formation of a blood clot (thrombus) in a deep vein leading to obstruction of blood flow. DVT is considered a major cardiovascular health risk and its early detection is considered crucial to patient survival and wellbeing. The present work describes the development and evaluation of two image analysis techniques for the identification of DVT in children. The first technique is based on entropy thresholding and the second on active contours. The robustness and sensitivity of these techniques have been evaluated both quantitatively as well as qualitatively. Towards that purpose error parameters were calculated using phantom and real patient images. The usefulness of our techniques was evaluated by specialist radiologists. The suggested algorithms were developed with the Matlabxc2xae simulation environment. Thus the present work has been implemented towards the improvement of pediatric patientsu0027 wellbeing and at the same time achieve early DVT prognosis.

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.