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

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

Automated detection of oral pre-cancerous tongue lesions using deep learning for early diagnosis of oral cavity cancer

no code implementations • 18 Sep 2019 • Mohammed Zubair M. Shamim, Sadatullah Syed, Mohammad Shiblee, Mohammed Usman, Syed Ali

Preliminary results using an (AI+Physician) ensemble model demonstrate that an automated initial screening process of tongue lesions using DCNNs can achieve near-human level classification performance for diagnosing early signs of OCC in patients.

General Classification Management +3