Sumit Chopra
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View article: Temporal Generalization: A Reality Check
Temporal Generalization: A Reality Check Open
Machine learning (ML) models often struggle to maintain performance under distribution shifts, leading to inaccurate predictions on unseen future data. In this work, we investigate whether and under what conditions models can achieve such …
View article: Leveraging Representation Learning for Bi-parametric Prostate MRI to Disambiguate PI-RADS 3 and Improve Biopsy Decision Strategies
Leveraging Representation Learning for Bi-parametric Prostate MRI to Disambiguate PI-RADS 3 and Improve Biopsy Decision Strategies Open
Objectives: Despite its high negative predictive value (NPV) for clinically significant prostate cancer (csPCa), MRI suffers from a substantial number of false positives, especially for intermediate-risk cases. In this work, we determine w…
View article: DIMCIM: A Quantitative Evaluation Framework for Default-mode Diversity and Generalization in Text-to-Image Generative Models
DIMCIM: A Quantitative Evaluation Framework for Default-mode Diversity and Generalization in Text-to-Image Generative Models Open
Recent advances in text-to-image (T2I) models have achieved impressive quality and consistency. However, this has come at the cost of representation diversity. While automatic evaluation methods exist for benchmarking model diversity, they…
View article: A Trust-Guided Approach to MR Image Reconstruction with Side Information
A Trust-Guided Approach to MR Image Reconstruction with Side Information Open
Reducing MRI scan times can improve patient care and lower healthcare costs. Many acceleration methods are designed to reconstruct diagnostic-quality images from sparse k-space data, via an ill-posed or ill-conditioned linear inverse probl…
View article: A Trust-Guided Approach to MR Image Reconstruction with Side Information
A Trust-Guided Approach to MR Image Reconstruction with Side Information Open
Reducing MRI scan times can improve patient care and lower healthcare costs. Many acceleration methods are designed to reconstruct diagnostic-quality images from sparse k-space data, via an ill-posed or ill-conditioned linear inverse probl…
View article: Embedding innovation: Insights from 10 years of the Clinician Educator Program (CEP) through eco-normalization evaluation
Embedding innovation: Insights from 10 years of the Clinician Educator Program (CEP) through eco-normalization evaluation Open
View article: Predicting Alzheimer’s Diseases and Related Dementias in 3‐year timeframe with AI Foundation Model on Electronic Health Records
Predicting Alzheimer’s Diseases and Related Dementias in 3‐year timeframe with AI Foundation Model on Electronic Health Records Open
Background As disease‐modifying interventions advance, there is a critical need to detect Alzheimer’s disease and related dementias (ADRD) at the earlier, pre‐symptomatic stages. Transformer is a powerful model used to understand high‐dime…
View article: HIST-AID: Leveraging Historical Patient Reports for Enhanced Multi-Modal Automatic Diagnosis
HIST-AID: Leveraging Historical Patient Reports for Enhanced Multi-Modal Automatic Diagnosis Open
Chest X-ray imaging is a widely accessible and non-invasive diagnostic tool for detecting thoracic abnormalities. While numerous AI models assist radiologists in interpreting these images, most overlook patients' historical data. To bridge…
View article: BURExtract-Llama: An LLM for Clinical Concept Extraction in Breast Ultrasound Reports
BURExtract-Llama: An LLM for Clinical Concept Extraction in Breast Ultrasound Reports Open
View article: Fine-Tuning In-House Large Language Models to Infer Differential Diagnosis from Radiology Reports
Fine-Tuning In-House Large Language Models to Infer Differential Diagnosis from Radiology Reports Open
Radiology reports summarize key findings and differential diagnoses derived from medical imaging examinations. The extraction of differential diagnoses is crucial for downstream tasks, including patient management and treatment planning. H…
View article: BURExtract-Llama: An LLM for Clinical Concept Extraction in Breast Ultrasound Reports
BURExtract-Llama: An LLM for Clinical Concept Extraction in Breast Ultrasound Reports Open
Breast ultrasound is essential for detecting and diagnosing abnormalities, with radiology reports summarizing key findings like lesion characteristics and malignancy assessments. Extracting this critical information is challenging due to t…
View article: A training regime to learn unified representations from complementary breast imaging modalities
A training regime to learn unified representations from complementary breast imaging modalities Open
Full Field Digital Mammograms (FFDMs) and Digital Breast Tomosynthesis (DBT) are the two most widely used imaging modalities for breast cancer screening. Although DBT has increased cancer detection compared to FFDM, its widespread adoption…
View article: Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction
Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction Open
Magnetic Resonance (MR) imaging, despite its proven diagnostic utility, remains an inaccessible imaging modality for disease surveillance at the population level. A major factor rendering MR inaccessible is lengthy scan times. An MR scanne…
View article: Jointly Modeling Inter- & Intra-Modality Dependencies for Multi-modal Learning
Jointly Modeling Inter- & Intra-Modality Dependencies for Multi-modal Learning Open
Supervised multi-modal learning involves mapping multiple modalities to a target label. Previous studies in this field have concentrated on capturing in isolation either the inter-modality dependencies (the relationships between different …
View article: Predicting Risk of Alzheimer’s Diseases and Related Dementias with AI Foundation Model on Electronic Health Records
Predicting Risk of Alzheimer’s Diseases and Related Dementias with AI Foundation Model on Electronic Health Records Open
Early identification of Alzheimer’s disease (AD) and AD-related dementias (ADRD) has high clinical significance, both because of the potential to slow decline through initiating FDA-approved therapies and managing modifiable risk factors, …
View article: FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging
FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging Open
Magnetic resonance imaging (MRI) has experienced remarkable advancements in the integration of artificial intelligence (AI) for image acquisition and reconstruction. The availability of raw k-space data is crucial for training AI models in…
View article: Efficient Detection of Ddos Attack Using Threshold Based Technique in Vanets
Efficient Detection of Ddos Attack Using Threshold Based Technique in Vanets Open
View article: Radiology Reports Improve Visual Representations Learned from Radiographs.
Radiology Reports Improve Visual Representations Learned from Radiographs. Open
Although human's ability to visually understand the structure of the World plays a crucial role in perceiving the World and making appropriate decisions, human perception does not solely rely on vision but amalgamates the information from …
View article: On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis
On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis Open
Magnetic Resonance Imaging (MRI) is considered the gold standard of medical imaging because of the excellent soft-tissue contrast exhibited in the images reconstructed by the MRI pipeline, which in-turn enables the human radiologist to dis…
View article: FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging.
FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging. Open
The fastMRI brain and knee dataset has enabled significant advances in exploring reconstruction methods for improving speed and image quality for Magnetic Resonance Imaging (MRI) via novel, clinically relevant reconstruction approaches. In…
View article: FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging
FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging Open
The fastMRI brain and knee dataset has enabled significant advances in exploring reconstruction methods for improving speed and image quality for Magnetic Resonance Imaging (MRI) via novel, clinically relevant reconstruction approaches. In…
View article: Microcontroller based logic control system for automated novel paddy straw bale combustor technology applied to greenhouse heating
Microcontroller based logic control system for automated novel paddy straw bale combustor technology applied to greenhouse heating Open
Automation of paddy straw bale combustor technology was achieved by designing a microcontroller based logic control system for controlling bale feeding, ensuring complete combustion of paddy straw bale and cleaning of the grate by removing…
View article: Microcontroller based logic control system for automated novel paddy straw bale combustor technology applied to greenhouse heating
Microcontroller based logic control system for automated novel paddy straw bale combustor technology applied to greenhouse heating Open
Automation of paddy straw bale combustor technology was achieved by designing a microcontroller based logic control system for controlling bale feeding, ensuring complete combustion of paddy straw bale and cleaning of the grate by removing…
View article: On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Disease
On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Disease Open
Early detection of many life-threatening diseases (e.g., prostate and breast cancer) within at-risk population can improve clinical outcomes and reduce cost of care. While numerous disease-specific "screening" tests that are closer to Poin…
View article: A No-Math Primer on the Principles of Machine Learning for Radiologists
A No-Math Primer on the Principles of Machine Learning for Radiologists Open
View article: Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs
Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs Open
View article: Virtualization In Cloud Computing : A Review
Virtualization In Cloud Computing : A Review Open
Cloud computing is one of the well developing fields in Computer Technology. Now days cloud computing is one of the fast growing technology because of online, cheap and pay as use scheme. Cloud Computing involves the concepts of parallel p…
View article: Generative Image Translation for Data Augmentation of Bone Lesion Pathology
Generative Image Translation for Data Augmentation of Bone Lesion Pathology Open
Insufficient training data and severe class imbalance are often limiting factors when developing machine learning models for the classification of rare diseases. In this work, we address the problem of classifying bone lesions from X-ray i…
View article: Deep neural network improves fracture detection by clinicians
Deep neural network improves fracture detection by clinicians Open
Significance Historically, computer-assisted detection (CAD) in radiology has failed to achieve improvements in diagnostic accuracy, decreasing clinician sensitivity and leading to unnecessary further diagnostic tests. With the advent of d…
View article: StarSpace: Embed All The Things!
StarSpace: Embed All The Things! Open
We present StarSpace, a general-purpose neural embedding model that can solve a wide variety of problems: labeling tasks such as text classification,ranking tasks such as information retrieval/web search,collaborative filtering-based or co…