Resilience of Named Entity Recognition Models under Adversarial Attack

no code implementations • NAACL (DADC) 2022 • Sudeshna Das, Jiaul Paik

Named entity recognition (NER) is a popular language processing task with wide applications.

Adversarial Attack named-entity-recognition +2

Cortical analysis of heterogeneous clinical brain MRI scans for large-scale neuroimaging studies

no code implementations • 2 May 2023 • Karthik Gopinath, Douglas N. Greve, Sudeshna Das, Steve Arnold, Colin Magdamo, Juan Eugenio Iglesias

Here we present the first method for cortical reconstruction, registration, parcellation, and thickness estimation for clinical brain MRI scans of any resolution and pulse sequence.

3D Reconstruction

Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets

no code implementations • 5 Sep 2022 • Benjamin Billot, Colin Magdamo, You Cheng, Steven E. Arnold, Sudeshna Das, Juan. E. Iglesias

Every year, millions of brain MRI scans are acquired in hospitals, which is a figure considerably larger than the size of any research dataset.

Brain Segmentation

Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham

1 code implementation • 5 May 2022 • Matthew Leming, Sudeshna Das, Hyungsoon Im

We also applied a method for quantifying uncertainty across an ensemble of these models to automatically exclude out-of-distribution data in the AD detection.

regression

Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and no Retraining

1 code implementation • 3 Mar 2022 • Benjamin Billot, Magdamo Colin, Sean E. Arnold, Sudeshna Das, Juan. E. Iglesias

We show that this method is considerably more robust than SynthSeg, while also outperforming cascaded networks and state-of-the-art segmentation denoising methods.

Denoising Image Segmentation +1

NeuraHealth: An Automated Screening Pipeline to Detect Undiagnosed Cognitive Impairment in Electronic Health Records with Deep Learning and Natural Language Processing

1 code implementation • 12 Jan 2022 • Tanish Tyagi, Colin G. Magdamo, Ayush Noori, Zhaozhi Li, Xiao Liu, Mayuresh Deodhar, Zhuoqiao Hong, Wendong Ge, Elissa M. Ye, Yi-han Sheu, Haitham Alabsi, Laura Brenner, Gregory K. Robbins, Sahar Zafar, Nicole Benson, Lidia Moura, John Hsu, Alberto Serrano-Pozo, Dimitry Prokopenko, Rudolph E. Tanzi, Bradley T. Hyman, Deborah Blacker, Shibani S. Mukerji, M. Brandon Westover, Sudeshna Das

Dementia related cognitive impairment (CI) is a neurodegenerative disorder, affecting over 55 million people worldwide and growing rapidly at the rate of one new case every 3 seconds.

Using Deep Learning to Identify Patients with Cognitive Impairment in Electronic Health Records

1 code implementation • 13 Nov 2021 • Tanish Tyagi, Colin G. Magdamo, Ayush Noori, Zhaozhi Li, Xiao Liu, Mayuresh Deodhar, Zhuoqiao Hong, Wendong Ge, Elissa M. Ye, Yi-han Sheu, Haitham Alabsi, Laura Brenner, Gregory K. Robbins, Sahar Zafar, Nicole Benson, Lidia Moura, John Hsu, Alberto Serrano-Pozo, Dimitry Prokopenko, Rudolph E. Tanzi, Bradley T. Hyman, Deborah Blacker, Shibani S. Mukerji, M. Brandon Westover, Sudeshna Das

Automated mining of these notes presents an opportunity to label patients with cognitive impairment in EHR data.

Natural Language Processing to Detect Cognitive Concerns in Electronic Health Records Using Deep Learning

no code implementations • 12 Nov 2020 • Zhuoqiao Hong, Colin G. Magdamo, Yi-han Sheu, Prathamesh Mohite, Ayush Noori, Elissa M. Ye, Wendong Ge, Haoqi Sun, Laura Brenner, Gregory Robbins, Shibani Mukerji, Sahar Zafar, Nicole Benson, Lidia Moura, John Hsu, Bradley T. Hyman, Michael B. Westover, Deborah Blacker, Sudeshna Das

Dementia is under-recognized in the community, under-diagnosed by healthcare professionals, and under-coded in claims data.