568 citations · 693 across the 20 of their papers we have counts for
20 papers
Meta Sparse Principal Component Analysis
Imon Banerjee, Jean Honorio
We study the meta-learning for support (i.e. the set of non-zero entries) recovery in high-dimensional Principal Component Analysis. We reduce the sufficient sample complexity in a…
OSCARS: An Outlier-Sensitive Content-Based Radiography Retrieval System
Xiaoyuan Guo, Jiali Duan, Saptarshi Purkayastha +3
Improving the retrieval relevance on noisy datasets is an emerging need for the curation of a large-scale clean dataset in the medical domain. While existing methods can be applied…
The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.5M Screening and Diagnostic Mammograms
Jiwoong J. Jeong, Brianna L. Vey, Ananth Reddy +13
Developing and validating artificial intelligence models in medical imaging requires datasets that are large, granular, and diverse. To date, the majority of publicly available bre…
MedShift: identifying shift data for medical dataset curation
Xiaoyuan Guo, Judy Wawira Gichoya, Hari Trivedi +2
To curate a high-quality dataset, identifying data variance between the internal and external sources is a fundamental and crucial step. However, methods to detect shift or varianc…
RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR
Yuyin Zhou, Shih-Cheng Huang, Jason Alan Fries +8
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 arch…
Two-step adversarial debiasing with partial learning -- medical image case-studies
Ramon Correa, Jiwoong Jason Jeong, Bhavik Patel +3
The use of artificial intelligence (AI) in healthcare has become a very active research area in the last few years. While significant progress has been made in image classification…
CVAD: A generic medical anomaly detector based on Cascade VAE
Xiaoyuan Guo, Judy Wawira Gichoya, Saptarshi Purkayastha +1
Detecting out-of-distribution (OOD) samples in medical imaging plays an important role for downstream medical diagnosis. However, existing OOD detectors are demonstrated on natural…
Margin-Aware Intra-Class Novelty Identification for Medical Images
Xiaoyuan Guo, Judy Wawira Gichoya, Saptarshi Purkayastha +1
Traditional anomaly detection methods focus on detecting inter-class variations while medical image novelty identification is inherently an intra-class detection problem. For examp…
Reading Race: AI Recognises Patient's Racial Identity In Medical Images
Imon Banerjee, Ananth Reddy Bhimireddy, John L. Burns +20
Background: In medical imaging, prior studies have demonstrated disparate AI performance by race, yet there is no known correlation for race on medical imaging that would be obviou…
PAC-Bayes Bounds on Variational Tempered Posteriors for Markov Models
Imon Banerjee, Vinayak A. Rao, Harsha Honnappa
Datasets displaying temporal dependencies abound in science and engineering applications, with Markov models representing a simplified and popular view of the temporal dependence s…
Generalization of Deep Convolutional Neural Networks -- A Case-study on Open-source Chest Radiographs
Nazanin Mashhaditafreshi, Amara Tariq, Judy Wawira Gichoya +1
Deep Convolutional Neural Networks (DCNNs) have attracted extensive attention and been applied in many areas, including medical image analysis and clinical diagnosis. One major cha…
Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with Similar Indications
Imon Banerjee, Priyanshu Sinha, Saptarshi Purkayastha +5
Purpose: Since the recent COVID-19 outbreak, there has been an avalanche of research papers applying deep learning based image processing to chest radiographs for detection of the…
A DICOM Framework for Machine Learning Pipelines against Real-Time Radiology Images
Pradeeban Kathiravelu, Puneet Sharma, Ashish Sharma +7
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 tr…
A Deep-learning Approach for Prognosis of Age-Related Macular Degeneration Disease using SD-OCT Imaging Biomarkers
Imon Banerjee, Luis de Sisternes, Joelle Hallak +4
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-…
A Scalable Machine Learning Approach for Inferring Probabilistic US-LI-RADS Categorization
Imon Banerjee, Hailey H. Choi, Terry Desser +1
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)…
Abstract: Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients
Imon Banerjee, Michael Francis Gensheimer, Douglas J. Wood +3
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 t…
Intelligent Word Embeddings of Free-Text Radiology Reports
Imon Banerjee, Sriraman Madhavan, Roger Eric Goldman +1
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…
Inferring Generative Model Structure with Static Analysis
Paroma Varma, Bryan He, Payal Bajaj +4
Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline. A popular solution is combining multiple sourc…
An Energy-Efficient VCO-Based Matrix Multiplier Block to Support On-Chip Image Analysis
Imon Banerjee, Arindam Sanyal
Images typically are represented as uniformly sampled data in the form of matrix of pixels/voxels. Therefore, matrix multiply-and-accumulate (MAC) forms the core of most state-of-t…
Computerized Multiparametric MR image Analysis for Prostate Cancer Aggressiveness-Assessment
Imon Banerjee, Lewis Hahn, Geoffrey Sonn +2
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…