arXiv subjects

Mrudang Mathur

Publications and source records attributed to Mrudang Mathur.

5 recordsLinked to original sources

arXiv2024

SurGen: Text-Guided Diffusion Model for Surgical Video Generation

Diffusion-based video generation models have made significant strides, producing outputs with improved visual fidelity, temporal coherence, and user control. These advancements hold great promise for improving surgical education by enabling more realistic, diverse, and interactive simulation environments. In this study, we introduce SurGen, a text-guided diffusion model tailored for surgical video synthesis. SurGen produces videos with the highest resolution and longest duration among existing surgical video generation models. We validate the visual and temporal quality of the outputs using standard image and video generation metrics. Additionally, we assess their alignment to the corresponding text prompts through a deep learning classifier trained on surgical data. Our results demonstrate the potential of diffusion models to serve as valuable educational tools for surgical trainees.

cs.CV↗

arXiv2024

A Generalist Model for Diverse Text-Guided Medical Image Synthesis

Deep learning algorithms require extensive data to achieve robust performance. However, data availability is often restricted in the medical domain due to patient privacy concerns. Synthetic data presents a possible solution to these challenges. Image generative models have found increasing use for medical applications, but are often task-specific, thus limiting their scalability. Moreover, existing models frequently rely on private datasets for training, which constrain their reproducibility. To address this, we introduce MediSyn: an open-access, generalist, text-guided latent diffusion model capable of generating synthetic images across 6 medical specialties and 10 imaging modalities, while being trained exclusively on publicly available data. Through extensive experimentation, we provide several key contributions. First, we demonstrate that training a generative model on visually diverse medical images does not degrade synthetic image quality. Second, we show that this generalist approach is substantially more computationally efficient than a coordinated suite of task-specific models. Third, we establish that a generalist model can produce realistic, text-aligned synthetic images across visually and medically distinct modalities, as validated by expert physicians. Fourth, we provide empirical evidence that these synthetic images are visually distinct from their corresponding real patient images, alleviating concerns about data memorization in image generative models. Finally, we demonstrate that a generalist model can produce synthetic images that improve classifier performance in data-limited settings across multiple medical specialties. Altogether, our findings highlight the immense potential of generalist image generative models to accelerate algorithmic research and development in medicine.

cs.CV↗

arXiv2023

A Generalizable Deep Learning System for Cardiac MRI

Cardiac MRI allows for a comprehensive assessment of myocardial structure, function and tissue characteristics. Here we describe a foundational vision system for cardiac MRI, capable of representing the breadth of human cardiovascular disease and health. Our deep-learning model is trained via self-supervised contrastive learning, in which visual concepts in cine-sequence cardiac MRI scans are learned from the raw text of the accompanying radiology reports. We train and evaluate our model on data from four large academic clinical institutions in the United States. We additionally showcase the performance of our models on the UK BioBank and two additional publicly available external datasets. We explore emergent capabilities of our system and demonstrate remarkable performance across a range of tasks, including the problem of left-ventricular ejection fraction regression and the diagnosis of 39 different conditions such as cardiac amyloidosis and hypertrophic cardiomyopathy. We show that our deep-learning system is capable of not only contextualizing the staggering complexity of human cardiovascular disease but can be directed towards clinical problems of interest, yielding impressive, clinical-grade diagnostic accuracy with a fraction of the training data typically required for such tasks.

eess.IV↗

arXiv2022

A Brief Note on Building Augmented Reality Models for Scientific Visualization

Augmented reality (AR) has revolutionized the video game industry by providing interactive, three-dimensional visualization. Interestingly, AR technology has only been sparsely used in scientific visualization. This is, at least in part, due to the significant technical challenges previously associated with creating and accessing such models. To ease access to AR for the scientific community, we introduce a novel visualization pipeline with which they can create and render AR models. We demonstrate our pipeline by means of finite element results, but note that our pipeline is generally applicable to data that may be represented through meshed surfaces. Specifically, we use two open-source software packages, ParaView and Blender. The models are then rendered through the platform, which we access through Android and iOS smartphones. To demonstrate our pipeline, we build AR models from static and time-series results of finite element simulations discretized with continuum, shell, and beam elements. Moreover, we openly provide python scripts to automate this process. Thus, others may use our framework to create and render AR models for their own research and teaching activities.

cs.GR↗

arXiv2020

Parametric Analysis of Smartphone Camera for a Low Cost Particle Image Velocimetry System

This study focuses on assessing smartphone camera characteristics for developing an economic smartphone-based Particle Image Velocimetry (PIV) system. In the investigation, flow around a cylinder was visualized using two commercially-available smartphones (OnePlus 5T and iPhone X) cameras and low-intensity laser diodes. Hydrogen bubbles generated from electrolysis (termed Bubble Image Velocimetry) of aluminum electrodes were used as seeding medium. OpenPIV, an open-source toolbox, was used for processing captured images and obtaining the flow fields. A parametric analysis of the two smartphones was conducted across varying camera characteristics such as ISO, exposure compensation and frame rate. The results obtained through experimentation were compared with the results of a validated computational fluid dynamics (CFD) study with the same flow conditions and were found to be in good agreement, with deviation ranging from 1% to 3.5% for iPhone X and 1% to 7% for OnePlus 5T. It was observed that a higher frame rate results in greater accuracy of the measurement. Further, an exposure compensation of -1 EV and an ISO of 400 was found to produce results with the least error as compared to CFD values.

eess.IV↗