MaxCorrMGNN: A Multi-Graph Neural Network Framework for Generalized Multimodal Fusion of Medical Data for Outcome Prediction

no code implementations • 13 Jul 2023 • Niharika S. D'Souza, Hongzhi Wang, Andrea Giovannini, Antonio Foncubierta-Rodriguez, Kristen L. Beck, Orest Boyko, Tanveer Syeda-Mahmood

With the emergence of multimodal electronic health records, the evidence for an outcome may be captured across multiple modalities ranging from clinical to imaging and genomic data.

Fusing Modalities by Multiplexed Graph Neural Networks for Outcome Prediction in Tuberculosis

no code implementations • 25 Oct 2022 • Niharika S. D'Souza, Hongzhi Wang, Andrea Giovannini, Antonio Foncubierta-Rodriguez, Kristen L. Beck, Orest Boyko, Tanveer Syeda-Mahmood

All these nuances make simple methods of early, late, or intermediate fusion of features inadequate for outcome prediction.

Extracting and Learning Fine-Grained Labels from Chest Radiographs

no code implementations • 18 Nov 2020 • Tanveer Syeda-Mahmood, K. C. L Wong, Joy T. Wu, M. D., M. P. H, Ashutosh Jadhav, Ph. D, Orest Boyko, M. D. Ph. D

Chest radiographs are the most common diagnostic exam in emergency rooms and intensive care units today.

Chest X-ray Report Generation through Fine-Grained Label Learning

no code implementations • 27 Jul 2020 • Tanveer Syeda-Mahmood, Ken C. L. Wong, Yaniv Gur, Joy T. Wu, Ashutosh Jadhav, Satyananda Kashyap, Alexandros Karargyris, Anup Pillai, Arjun Sharma, Ali Bin Syed, Orest Boyko, Mehdi Moradi

Obtaining automated preliminary read reports for common exams such as chest X-rays will expedite clinical workflows and improve operational efficiencies in hospitals.