Imon Banerjee

TL;DR: Different data fusion techniques that can be applied to combine medical imaging with EHR, and systematically review medical data fusion literature published between 2012 and 2020 are described.

TL;DR: A controlled longitudinal weight perturbation study combining multiple omics strategies during periods of weight gain and loss in humans demonstrated that weight gain is associated with the activation of strong inflammatory and hypertrophic cardiomyopathy signatures in blood.

TL;DR: Investigation of cutting-edge deep learning methods for information extraction from medical imaging free text reports at a multi-institutional scale and compares them to the state-of-the-art domain-specific rule-based system - PEFinder and traditional machine learning methods - SVM and Adaboost suggests feasibility of broader usage of neural network models in automated classification of multi-Institutional imaging text reports.

TL;DR: The PENet model could be applied as a triage tool to automatically identify clinically important PEs allowing for prioritization for diagnostic radiology interpretation and improved care pathways via more efficient diagnosis.

TL;DR: This study developed and compared different multimodal fusion model architectures that are capable of utilizing both pixel data from volumetric Computed Tomography Pulmonary Angiography scans and clinical patient data from the EMR to automatically classify Pulmonary Embolism cases.