Search Results for author: Imon Banerjee

Found 29 papers, 5 papers with code

Assessing Empathy in Large Language Models with Real-World Physician-Patient Interactions

no code implementations26 May 2024 Man Luo, Christopher J. Warren, Lu Cheng, Haidar M. Abdul-Muhsin, Imon Banerjee

The integration of Large Language Models (LLMs) into the healthcare domain has the potential to significantly enhance patient care and support through the development of empathetic, patient-facing chatbots.

Multivariate Analysis on Performance Gaps of Artificial Intelligence Models in Screening Mammography

no code implementations8 May 2023 Linglin Zhang, Beatrice Brown-Mulry, Vineela Nalla, InChan Hwang, Judy Wawira Gichoya, Aimilia Gastounioti, Imon Banerjee, Laleh Seyyed-Kalantari, Minjae Woo, Hari Trivedi

However, after controlling for confounding, we found lower FN risk associates with Other race(RR=0. 828;p=. 050), biopsy-proven benign lesions(RR=0. 927;p=. 011), and mass(RR=0. 921;p=. 010) or asymmetry(RR=0. 854;p=. 040); higher FN risk associates with architectural distortion (RR=1. 037;p<. 001).

Breast Cancer Detection

Generalizable Natural Language Processing Framework for Migraine Reporting from Social Media

no code implementations23 Dec 2022 Yuting Guo, Swati Rajwal, Sahithi Lakamana, Chia-Chun Chiang, Paul C. Menell, Adnan H. Shahid, Yi-Chieh Chen, Nikita Chhabra, Wan-Ju Chao, Chieh-Ju Chao, Todd J. Schwedt, Imon Banerjee, Abeed Sarker

In this paper, we (i) verify that there is substantial migraine-related chatter available on social media (Twitter and Reddit), self-reported by migraine sufferers; (ii) develop a platform-independent text classification system for automatically detecting self-reported migraine-related posts, and (iii) conduct analyses of the self-reported posts to assess the utility of social media for studying this problem.

Management text-classification +1

Meta Sparse Principal Component Analysis

no code implementations18 Aug 2022 Imon Banerjee, Jean Honorio

We assume each task to be a different random Principal Component (PC) matrix with a possibly different support and that the support union of the PC matrices is small.


Augmenting Vision Language Pretraining by Learning Codebook with Visual Semantics

no code implementations31 Jul 2022 Xiaoyuan Guo, Jiali Duan, C. -C. Jay Kuo, Judy Wawira Gichoya, Imon Banerjee

Language modality within the vision language pretraining framework is innately discretized, endowing each word in the language vocabulary a semantic meaning.

Language Modelling Masked Language Modeling

Advances in Prediction of Readmission Rates Using Long Term Short Term Memory Networks on Healthcare Insurance Data

no code implementations30 Jun 2022 Shuja Khalid, Francisco Matos, Ayman Abunimer, Joel Bartlett, Richard Duszak, Michal Horny, Judy Gichoya, Imon Banerjee, Hari Trivedi

We developed a bi-directional Long Short Term Memory (LSTM) Network that is able to use readily available insurance data (inpatient visits, outpatient visits, and drug prescriptions) to predict 30 day re-admission for any admitted patient, regardless of reason.

Multimodal spatiotemporal graph neural networks for improved prediction of 30-day all-cause hospital readmission

1 code implementation14 Apr 2022 Siyi Tang, Amara Tariq, Jared Dunnmon, Umesh Sharma, Praneetha Elugunti, Daniel Rubin, Bhavik N. Patel, Imon Banerjee

Measures to predict 30-day readmission are considered an important quality factor for hospitals as accurate predictions can reduce the overall cost of care by identifying high risk patients before they are discharged.

Graph Neural Network Readmission Prediction

MedShift: identifying shift data for medical dataset curation

no code implementations27 Dec 2021 Xiaoyuan Guo, Judy Wawira Gichoya, Hari Trivedi, Saptarshi Purkayastha, Imon Banerjee

Given an internal dataset A as the base source, we first train anomaly detectors for each class of dataset A to learn internal distributions in an unsupervised way.

RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR

no code implementations23 Nov 2021 Yuyin Zhou, Shih-Cheng Huang, Jason Alan Fries, Alaa Youssef, Timothy J. Amrhein, Marcello Chang, Imon Banerjee, Daniel Rubin, Lei Xing, Nigam Shah, Matthew P. Lungren

Despite the routine use of electronic health record (EHR) data by radiologists to contextualize clinical history and inform image interpretation, the majority of deep learning architectures for medical imaging are unimodal, i. e., they only learn features from pixel-level information.

Benchmarking Computed Tomography (CT) +2

Margin-Aware Intra-Class Novelty Identification for Medical Images

1 code implementation31 Jul 2021 Xiaoyuan Guo, Judy Wawira Gichoya, Saptarshi Purkayastha, Imon Banerjee

Traditional anomaly detection methods focus on detecting inter-class variations while medical image novelty identification is inherently an intra-class detection problem.

Anomaly Detection Novelty Detection

Reading Race: AI Recognises Patient's Racial Identity In Medical Images

no code implementations21 Jul 2021 Imon Banerjee, Ananth Reddy Bhimireddy, John L. Burns, Leo Anthony Celi, Li-Ching Chen, Ramon Correa, Natalie Dullerud, Marzyeh Ghassemi, Shih-Cheng Huang, Po-Chih Kuo, Matthew P Lungren, Lyle Palmer, Brandon J Price, Saptarshi Purkayastha, Ayis Pyrros, Luke Oakden-Rayner, Chima Okechukwu, Laleh Seyyed-Kalantari, Hari Trivedi, Ryan Wang, Zachary Zaiman, Haoran Zhang, Judy W Gichoya

Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race.

PAC-Bayes Bounds on Variational Tempered Posteriors for Markov Models

no code implementations13 Jan 2021 Imon Banerjee, Vinayak A. Rao, Harsha Honnappa

We present a PAC-Bayesian analysis of variational Bayes (VB) approximations to tempered Bayesian posterior distributions, bounding the model risk of the VB approximations.

Statistics Theory Statistics Theory

Generalization of Deep Convolutional Neural Networks -- A Case-study on Open-source Chest Radiographs

no code implementations11 Jul 2020 Nazanin Mashhaditafreshi, Amara Tariq, Judy Wawira Gichoya, Imon Banerjee

The results show the internal performance of each of the 5 pathologies outperformed external performance on both of the models.

A DICOM Framework for Machine Learning Pipelines against Real-Time Radiology Images

1 code implementation16 Apr 2020 Pradeeban Kathiravelu, Puneet Sharma, ASHISH SHARMA, Imon Banerjee, Hari Trivedi, Saptarshi Purkayastha, Priyanshu Sinha, Alexandre Cadrin-Chenevert, Nabile Safdar, Judy Wawira Gichoya

Executing machine learning (ML) pipelines in real-time on radiology images is hard due to the limited computing resources in clinical environments and the lack of efficient data transfer capabilities to run them on research clusters.

BIG-bench Machine Learning

A Deep-learning Approach for Prognosis of Age-Related Macular Degeneration Disease using SD-OCT Imaging Biomarkers

no code implementations27 Feb 2019 Imon Banerjee, Luis de Sisternes, Joelle Hallak, Theodore Leng, Aaron Osborne, Mary Durbin, Daniel Rubin

We propose a hybrid sequential deep learning model to predict the risk of AMD progression in non-exudative AMD eyes at multiple timepoints, starting from short-term progression (3-months) up to long-term progression (21-months).

A Scalable Machine Learning Approach for Inferring Probabilistic US-LI-RADS Categorization

no code implementations15 Jun 2018 Imon Banerjee, Hailey H. Choi, Terry Desser, Daniel L. Rubin

We propose a scalable computerized approach for large-scale inference of Liver Imaging Reporting and Data System (LI-RADS) final assessment categories in narrative ultrasound (US) reports.

BIG-bench Machine Learning

Abstract: Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients

no code implementations9 Jan 2018 Imon Banerjee, Michael Francis Gensheimer, Douglas J. Wood, Solomon Henry, Daniel Chang, Daniel L. Rubin

We propose a deep learning model - Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) for estimating short-term life expectancy (3 months) of the patients by analyzing free-text clinical notes in the electronic medical record, while maintaining the temporal visit sequence.

Intelligent Word Embeddings of Free-Text Radiology Reports

1 code implementation19 Nov 2017 Imon Banerjee, Sriraman Madhavan, Roger Eric Goldman, Daniel L. Rubin

Radiology reports are a rich resource for advancing deep learning applications in medicine by leveraging the large volume of data continuously being updated, integrated, and shared.

Word Embeddings

Inferring Generative Model Structure with Static Analysis

no code implementations NeurIPS 2017 Paroma Varma, Bryan He, Payal Bajaj, Imon Banerjee, Nishith Khandwala, Daniel L. Rubin, Christopher Ré

Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline.

Computerized Multiparametric MR image Analysis for Prostate Cancer Aggressiveness-Assessment

no code implementations1 Dec 2016 Imon Banerjee, Lewis Hahn, Geoffrey Sonn, Richard Fan, Daniel L. Rubin

We propose an automated method for detecting aggressive prostate cancer(CaP) (Gleason score >=7) based on a comprehensive analysis of the lesion and the surrounding normal prostate tissue which has been simultaneously captured in T2-weighted MR images, diffusion-weighted images (DWI) and apparent diffusion coefficient maps (ADC).

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