Surya Prasath

Surya Prasath
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Surya verified their affiliation via an institutional email.
Verified
Surya verified their affiliation via an institutional email.
Cincinnati Children's Hospital Medical Center | CCHMC · Division of Biomedical Informatics

PhD
For collaborative works contact at surya.iit@gmail.com and see our AI Lab for more details: https://www.prasathlab.com

About

264
Publications
149,868
Reads
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4,937
Citations
Introduction
Applied Mathematician with substantial knowledge in AI, image processing, computer vision, machine learning, and data science. Also work on a variety of other problems that are mathematically interesting and fun to do. Always looking forward to do collaborative work and interested in making connections in different data science areas. For positions in our lab see: https://www.prasathlab.com/about/positions. Contact me at surya.iit@gmail.com More details: http://prasathlab.com
Additional affiliations
March 2018 - present
University of Cincinnati
Position
  • Professor (Assistant)
June 2018 - present
University of Cincinnati
Position
  • Professor (Assistant)
March 2018 - present
Cincinnati Children's Hospital Medical Center
Position
  • Professor (Assistant)
Education
August 2004 - November 2009
Indian Institute of Technology Madras
Field of study
  • Mathematics
August 2002 - May 2004
Indian Institute of Technology Madras
Field of study
  • Mathematics

Publications

Publications (264)
Chapter
Full-text available
Histopathological image analysis remains at the forefront of computational pathology presenting numerous challenges and demanding tasks, primarily due to the complex nature of tissue structures and the extensive scale of whole slide images (WSIs). Deep learning models have been widely used in histopathology image analysis, especially convolutional...
Article
The advent of deep learning (DL) and multimodal spatial transcriptomics (ST) has revolutionized cancer research, offering unprecedented insights into tumor biology. This book chapter explores the integration of DL with ST to advance cancer diagnostics, treatment planning, and precision medicine. DL, a subset of artificial intelligence, employs neur...
Article
Full-text available
Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic spectrum. However, due to sensor limitations and imperfections during the image acquisition and transmission phases, noise is introduced into the acquired image, which can have a negative impact on dow...
Article
Full-text available
Electroencephalography (EEG) is a well-known modality in neuroscience and is widely used in identifying and classifying neurological disorders. This paper investigates how EEG data can be used along with knowledge distillation-based deep learning models to detect mental disorders like epilepsy and sleep disorders. The EEG signals are converted into...
Article
Full-text available
In the field of histopathology, many studies on the classification of whole slide images (WSIs) using artificial intelligence (AI) technology have been reported. We have studied the disease progression assessment of glioma. Adult-type diffuse gliomas, a type of brain tumor, are classified into astrocytoma, oligodendroglioma, and glioblastoma. Astro...
Article
Full-text available
Digital images are corrupted with noise, and image denoising is an important step in image processing modules. In this review, the latest developments in filtering methods for color image restoration are analyzed. These algorithms are compared in terms of objective image quality measures and divided into major classes, such as spatial domain, switc...
Preprint
Background: Accurate identification of inflammatory cells from mucosal histopathology images is important in diagnosing ulcerative colitis. The identification of eosinophils in the colonic mucosa has been associated with disease course. Cell counting is not only time-consuming but can also be subjective to human biases. In this study we developed a...
Article
Full-text available
This study aims to establish a method with ordinary videos (without special equipment) for gait quality assessment. The treatment for Cerebral Palsy, which is a movement disorder, requires gait quality assessment routinely. However, the current assessment methods need expensive equipment and high technical knowledge of rehabilitation. This paper ai...
Preprint
Full-text available
Text extraction is a highly subjective problem which depends on the dataset that one is working on and the kind of summarization details that needs to be extracted out. All the steps ranging from preprocessing of the data, to the choice of an optimal model for predictions, depends on the problem and the corpus at hand. In this paper, we describe a...
Article
Background There has been an increase in the development of both machine learning (ML) and deep learning (DL) prediction models in Inflammatory Bowel Disease. We aim in this systematic review to assess the methodological quality and risk of bias of ML and DL IBD image-based prediction studies. Methods We searched three databases, PubMed, Scopus a...
Article
Full-text available
Three-dimensional (3D) ultrasound echo decorrelation imaging can successfully monitor treatment of liver tumors by radiofrequency ablation (RFA), but has limitations in mapping ablation zones and tissue temperature. Here, supervised deep learning was investigated to improve prediction of temperature and ablation from 3D echo decorrelation images. R...
Preprint
Full-text available
Background and Aims We previously reported clinical features associated with outcomes in pediatric ulcerative colitis (UC). Here we developed a histopathology model to predict corticosteroid-free remission (CSFR) on mesalamine therapy alone. Methods Pre-treatment rectal biopsy slides were digitized in training and validation groups of 292 and 113...
Article
Full-text available
The evolution of poliomyelitis (polio) research, an acute viral infection predominantly affecting children and ranging from mild illness to disabling paralysis, was systematically evaluated through a bibliometric analysis of publications from 1857 to 2019. Six thousand one hundred thirty-nine polio-related publications were extracted from the Web o...
Article
Full-text available
Dengue virus, a paramount public health concern, prompts ample global research. This paper provides a comprehensive overview of global efforts in dengue research, applying bibliometric and scientometric procedures to examine the breadth and depth of this field. Drawing data from the Web of Science (WoS) and Scopus databases, 18,607 publications fro...
Article
Full-text available
Morse code is one of the oldest communication techniques and used in telecommunication systems. Morse code can be transmitted as a visual signal by using reflections or with the help of flashlights, but it can also be used as a non-detectable form of communication by using the tapping of fingers or even blinking of eyes. In this paper, we develop a...
Article
Full-text available
Background Identification of pathogenic bacteria from clinical specimens and evaluating their antimicrobial resistance (AMR) are laborious tasks that involve in vitro cultivation, isolation, and susceptibility testing. Recently, a number of methods have been developed that use machine learning algorithms applied to the whole-genome sequencing data...
Article
Full-text available
Over the past few decades, a lot of new neural network architectures and deep learning (DL)-based models have been developed to tackle problems more efficiently, rapidly, and accurately. For classification problems, it is typical to utilize fully connected layers as the network head. These dense layers used in such architectures have always remaine...
Article
Full-text available
Noise is an unwanted element that degrades the quality of digital images. Salt and pepper noise is a type of noise that is introduced in one or more steps during image acquisition, enrolment, or transmission. It is therefore important to apply superior restoration methods to mitigate the noise. In this paper, a novel distance- and intensity-based s...
Article
Full-text available
Cancerous skin lesions are one of the deadliest diseases that have the ability in spreading across other body parts and organs. Conventionally, visual inspection and biopsy methods are widely used to detect skin cancers. However, these methods have some drawbacks, and the prediction is not highly accurate. This is where a dependable automatic recog...
Article
Full-text available
Usability is one of the most important characteristics of software applications, especially when it comes to mobile shopping applications. There is a great deal of shift from traditional shopping to online shopping because it benefits both parties i.e., customers as well as businessmen. In such a scenario, the usability factor can play a very vital...
Article
Full-text available
Transcription factors read the genome, fundamentally connecting DNA sequence to gene expression across diverse cell types. Determining how, where, and when TFs bind chromatin will advance our understanding of gene regulatory networks and cellular behavior. The 2017 ENCODE-DREAM in vivo Transcription-Factor Binding Site (TFBS) Prediction Challenge h...
Article
BACKGROUND The majority of children with ulcerative colitis present with extensive colitis at diagnosis, but the response to therapy is heterogenous. Identifying the optimal window for biologic treatment and risk stratification of patients remains an unmet need. AIM To develop a pathology based histomic model to predict corticosteroid free clinica...
Article
Full-text available
Decisively delineating cell identities from uni- and multimodal single-cell datasets is complicated by diverse modalities, clustering methods, and reference atlases. We describe scTriangulate, a computational framework to mix-and-match multiple clustering results, modalities, associated algorithms, and resolutions to achieve an optimal solution. Ra...
Article
An improved understanding of the human lung necessitates advanced systems models informed by an ever-increasing repertoire of molecular omics, cellular, imaging, and pathological datasets. To centralize and standardize information across broad lung research efforts we expanded the LungMAP.net website into a new gateway portal. This portal connects...
Article
Full-text available
Impact: Provide an overview of bronchopulmonary dysplasia, its definitions, and their shortcomings. Explore the areas where machine learning may be used to further our understanding of bronchopulmonary dysplasia.
Article
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In this work, we address the problem of piecewise affine approximations, that is, to find piecewise affine functions that well-approximate a given signal. The proposed approach is optimal in the sense of L2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepacka...
Article
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Nonlinear evolution equations are used to model various complex physical phenomena, and finding exact solutions of these equations is important for their applications. One of the important problem is that of finding traveling wave solutions in well-known nonlinear evolution systems from mathematical physics. In this paper, the differential transfor...
Preprint
Full-text available
We propose here a proof of existence of a minimizer of a segmentation functional based on a priori information on target shapes, and formulated with level sets. The existence of a minimizer is very important, because it guarantees the convergence of any numerical methods (either gradient descents techniques and variants, or PDE resolutions) used to...
Chapter
Full-text available
The ancient game of ṭāb is a war and race game. It is played by two teams, each consisting of at least one player. In addition to presenting the game and its rules, the authors develop three versions of the game: human versus human, human versus computer, and computer versus computer. The authors employ a Genetic Algorithm (GA) to help the computer...
Book
Full-text available
The COVID-19 pandemic has significantly affected the healthcare sector across the globe. Artificial Intelligence (AI) and the Internet of Medical Things (IoMT) play important roles when dealing with emerging challenges. These technologies are being applied to problems involving the early detection of infections, fast contact tracing, decision-makin...
Article
Full-text available
Cells and tissues respond to perturbations in multiple ways that can be sensitively reflected in the alterations of gene expression. Current approaches to finding and quantifying the effects of perturbations on cell-level responses over time disregard the temporal consistency of identifiable gene programs. To leverage the occurrence of these patter...
Article
Full-text available
Storage and transmission of high-compression 3D radiological images that create high-quality reconstruction upon decompression are critical necessities for effective and efficient teleradiology. To cater to this need, we propose a near lossless 3D image volume compression method based on optimal multilinear singular value decomposition called “3D-V...
Poster
Full-text available
Most disease-associated genetic variants fall outside of protein-coding DNA and are often enriched in regulatory elements associated with DNA binding proteins known as transcription factors (TFs). Computational methods are largely used to predict TF binding sites (TFBS) as the experimental characterization of most human TFs is intractable due to te...
Preprint
Full-text available
Federated learning (FL) is a system in which a central aggregator coordinates the efforts of multiple clients to solve machine learning problems. This setting allows training data to be dispersed in order to protect privacy. The purpose of this paper is to provide an overview of FL systems with a focus on healthcare. FL is evaluated here based on i...
Book
Full-text available
This book was created with the intention of informing an international audience about the latest technological aspects for developing smart agricultural applications. As artificial intelligence (AI) takes the main role in this, the majority of the chapters are associated with the role of AI and data analytics components for better agricultural appl...
Article
Introduction: Most children with UC present with extensive colitis at diagnosis, but the response to therapy is heterogenous. Identifying the optimal window for biologic treatment and risk stratification of patients remains an unmet need. The `Predictors of Response to Standardized Pediatric Colitis Therapy' (PROTECT) study identified a combination...
Article
Full-text available
Image registration is a critical component in the applications of various medical image analyses. In recent years, there has been a tremendous surge in the development of deep learning (DL)-based medical image registration models. This paper provides a comprehensive review of medical image registration. Firstly, a discussion is provided for supervi...
Preprint
Full-text available
Image registration is a critical component in the applications of various medical image analyses. In recent years, there has been a tremendous surge in the development of deep learning (DL)-based medical image registration models. This paper provides a comprehensive review of medical image registration. Firstly, a discussion is provided for supervi...
Article
Full-text available
Federated learning (FL) refers to a system in which a central aggregator coordinates the efforts of several clients to solve the issues of machine learning. This setting allows the training data to be dispersed in order to protect the privacy of each device. This paper provides an overview of federated learning systems, with a focus on healthcare....
Preprint
Full-text available
Cells and tissues respond to perturbations in multiple ways that can be sensitively reflected in alterations of gene expression. Current approaches to finding and quantifying the effects of perturbations on cell-level responses over time disregard the temporal consistency of identifiable gene programs. To leverage the occurrence of these patterns f...
Article
Full-text available
Dementia is one of the leading causes of severe cognitive decline, it induces memory loss and impairs the daily life of millions of people worldwide. In this work, we consider the classification of dementia using magnetic resonance (MR) imaging and clinical data with machine learning models. We adapt univariate feature selection in the MR data pre-...
Article
Full-text available
The human lung is a complex organ with high cellular heterogeneity, and its development and maintenance require interactive gene networks and dynamic cross-talk among multiple cell types. We focus on the confocal immunofluorescent (IF) images of lung tissues from the LungMAP database to reveal lung development. Using the current state-of-the-art de...
Conference Paper
Full-text available
The emphasis of this article is on the data-driven diagnosis of polycystic ovary syndrome (PCOS) in women. Data from the Kaggle repository is used to train ensemble machine learning algorithms. There are 177 women with PCOS in this dataset, which includes 43 different characteristics. To begin, a univariate feature selection and feature elimination...
Preprint
Full-text available
Transcription factors read the genome, fundamentally connecting DNA sequence to gene expression across diverse cell types. Determining how, where, and when TFs bind chromatin will advance our understanding of gene regulatory networks and cellular behavior. The 2017 ENCODE-DREAM in vivo Transcription-Factor Binding Site ( TFBS ) Prediction Challenge...
Article
Full-text available
Biometric recognition based on the full face is an extensive research area. However, using only partially visible faces, such as in the case of veiled-persons, is a challenging task. Deep convolutional neural network (CNN) is used in this work to extract the features from veiled-person face images. We found that the sixth and the seventh fully conn...
Article
Full-text available
In this paper, we present an efficient dermoscopic image segmentation method based on the linearisation of gamma-correction, and convolutional neural networks. Linearisation of gamma-correction is helpful to enhance low-intensity regions of skin lesion areas. Therefore, postprocessing tasks can work more effectively. The proposed convolutional neur...
Preprint
Full-text available
An improved understanding of the human lung necessitates advanced systems models informed by an ever-increasing repertoire of molecular omics, cellular, imaging and pathological datasets. To centralize and standardize information across broad lung research efforts we expanded the LungMAP.net website into a gateway portal. This portal connects a bro...
Article
Full-text available
Image denoising methods are of fundamental importance in image processing and artificial intelligence systems. In this review, we analyze the traditional and state of the art mathematical models for computational color image denoising. These algorithms are divided into methods that are based on the partial differential equations, low rank, sparse r...
Article
Full-text available
Knowledge of the anatomy of each tissue and the relationships between progenitors and daughter cells is necessary to understand physiology and pathology. The anatomy of hematopoiesis in the marrow remains largely unknown. Here we identify strategies to image all steps of blood cell production in the mouse sternum using confocal microscopy. We show...
Article
Full-text available
Biometric recognition based on the full face is an extensive research area. However, using only partially visible faces, such as in the case of veiledpersons, is a challenging task. Deep convolutional neural network (CNN) is used in this work to extract the features from veiled-person face images. We found that the sixth and the seventh fully conne...
Preprint
Full-text available
Biometric recognition based on the full face is an extensive research area. However, using only partially visible faces, such as in the case of veiled-persons, is a challenging task. Deep convolutional neural network (CNN) is used in this work to extract the features from veiled-person face images. We found that the sixth and the seventh fully conn...
Preprint
Full-text available
Hundreds of bioinformatics approaches now exist to define cellular heterogeneity from single-cell genomics data. Reconciling conflicts between diverse methods, algorithm settings, annotations or modalities have the potential to clarify which populations are real and establish reusable reference atlases. Here, we present a customizable computational...
Article
Full-text available
Volume estimation of brain tissues such as the White Matter, Gray Matter and Cerebrospinal Fluid is an important task in brain image analysis and also used to diagnose neurological and psychiatric disorders. In this work, brain tissue volume reduction is estimated to detect Alzheimer’s disease (AD) using magnetic resonance images. The proposed meth...
Article
Full-text available
Background In critically ill infants, the position of a peripherally inserted central catheter (PICC) must be confirmed frequently, as the tip may move from its original position and run the risk of hyperosmolar vascular damage or extravasation into surrounding spaces. Automated detection of PICC tip position holds great promise for alerting bedsid...
Preprint
Full-text available
The outbreak of novel coronavirus disease (COVID- 19) has claimed millions of lives and has affected all aspects of human life. This paper focuses on the application of deep learning (DL) models to medical imaging and drug discovery for managing COVID-19 disease. In this article, we detail various medical imaging-based studies such as X-rays and co...
Article
Full-text available
The outbreak of novel coronavirus disease (COVID-19) has claimed millions of lives and has affected all aspects of human life. This paper focuses on the application of deep learning (DL) models to medical imaging and drug discovery for managing COVID-19 disease. In this article, we detail various medical imaging-based studies such as X-rays and com...
Article
Full-text available
Having control over your data is a right and a duty that every citizen has in our digital society. It is often that users skip entire policies of applications or websites to save time and energy without realizing the potential sticky points in these policies. Due to obscure language and verbose explanations majority of users of hypermedia do not bo...
Article
Full-text available
This paper focuses on the application of deep learning (DL) based model in the analysis of novel coronavirus disease (COVID-19) from X-ray images. The novelty of this work is in the development of a new DL algorithm termed as optimized residual network (CO-ResNet) for COVID-19. The proposed CO-ResNet is developed by applying hyperparameter tuning t...
Cover Page
Full-text available
COVID-19 pandemic has significantly changed our lives. Countries are introducing social distancing measures, including lockdown, to avoid the spread of infection. Organisations are implementing new ways of continuing their business by facilitating work from home and avoiding contacts using the Internet of Medical Things (IoMT), Internet of Healthca...