Attention-based Multi-scale Gated Recurrent Encoder with Novel Correlation Loss for COVID-19 Progression Prediction

no code implementations • 18 Jul 2021 • Aishik Konwer, Joseph Bae, Gagandeep Singh, Rishabh Gattu, Syed Ali, Jeremy Green, Tej Phatak, Prateek Prasanna

This vector is used as an input to a decoder module to predict patch severity grades at a future timepoint.

Transfer Learning

Attention based CNN-LSTM Network for Pulmonary Embolism Prediction on Chest Computed Tomography Pulmonary Angiograms

no code implementations • 13 Jul 2021 • Sudhir Suman, Gagandeep Singh, Nicole Sakla, Rishabh Gattu, Jeremy Green, Tej Phatak, Dimitris Samaras, Prateek Prasanna

In this study we propose a two-stage attention-based CNN-LSTM network for predicting PE, its associated type (chronic, acute) and corresponding location (leftsided, rightsided or central) on computed tomography (CT) examinations.

Computed Tomography (CT)

Predicting Clinical Outcomes in COVID-19 using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study

no code implementations • 15 Jul 2020 • Joseph Bae, Saarthak Kapse, Gagandeep Singh, Rishabh Gattu, Syed Ali, Neal Shah, Colin Marshall, Jonathan Pierce, Tej Phatak, Amit Gupta, Jeremy Green, Nikhil Madan, Prateek Prasanna

Radiomic and DL classification models had mAUCs of 0. 78+/-0. 02 and 0. 81+/-0. 04, compared with expert scores mAUCs of 0. 75+/-0. 02 and 0. 79+/-0. 05 for mechanical ventilation requirement and mortality prediction, respectively.

Decision Making Mortality Prediction