Deval Mehta

Towards Objective Obstetric Ultrasound Assessment: Contrastive Representation Learning for Fetal Movement Detection

Talha Ilyas, Duong Binh Nhu, Allison S. Thomas, Arie Levin et autres

Accurate fetal movement (FM) detection is essential for assessing prenatal health, as abnormal movement patterns can indicate underlying complications such as placental dysfunction or fetal distress. Traditional methods, including maternal perception and cardiotocography (CTG), suffer from subjectivity and limited accuracy. To address …

Automated Quantification of Lens Cortex and Nuclear Opacity Based on Swept-Source Anterior Segment Optical Coherence Tomography

Xiaotong Han, Xin Zhang, Jiaqing Zhang, Haowen Lin et autres

PURPOSE: To develop and validate an automated lens cortex and nuclear opacity quantification method based on swept-source anterior segment optical coherence tomography (AS-OCT). METHODS: This cross-sectional study included 504 cataract surgery candidates. Lens images were captured using swept-source AS-OCT (CASIA-2; Tomey Corporation). …

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Multimodal CT Perfusion–Based Deep Learning for Predicting Stroke Lesion Outcomes in Complete and No Recanalization Scenarios

Yasmeen George, Deval Mehta, Longting Lin, Chushuang Chen et autres

ABSTRACT BACKGROUND AND PURPOSE: Predicting the final location and volume of lesions in acute ischemic stroke (AIS) is crucial for clinical management. While CT perfusion (CTP) imaging is routinely used for estimating lesion outcomes, conventional threshold-based methods have limitations. We developed specialized …

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Prevalence and Ethnic Distribution of Sickle Cell Trait and Sickle Cell Anemia in the Saurashtra Region: A Cross-Sectional Study

Dhara P. Trivedi, Deval Mehta, Punithan Narayanan, Pragati Kantibhai Bhimani et autres

Background: Sickle cell disorders represent a significant public health concern in India, with varying prevalence across different ethnic groups and geographical regions. Understanding regional distribution patterns is crucial for implementing effective screening and management programs. Objective: This study aimed to investigate the …

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Adaptive transformer modelling of density function for nonparametric survival analysis

Xin Zhang, Deval Mehta, Yanan Hu, Chao Zhu et autres

Abstract Survival analysis holds a crucial role across diverse disciplines, such as economics, engineering and healthcare. It empowers researchers to analyze both time-invariant and time-varying data, encompassing phenomena like customer churn, material degradation and various medical outcomes. Given the complexity and heterogeneity …

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Correlation between Mean Corpuscular Hemoglobin, Mean Corpuscular Hemoglobin Concentration, and Fetal Hemoglobin Levels in Sickle Cell Anemia Patients of Saurashtra Region in Gujarat

Dhara P. Trivedi, Deval Mehta, Punithan Narayanan, Pragati Kantibhai Bhimani et autres

Abstract Introduction: Sickle cell anemia (SCA) is a genetic disorder characterized by abnormal hemoglobin S production. Fetal hemoglobin (HbF) levels and complete blood count (CBC) parameters are crucial in evaluating SCA. This study investigates the correlations between mean corpuscular hemoglobin (MCH), MCH …

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IMPROVE: Improving Medical Plausibility without Reliance on HumanValidation -- An Enhanced Prototype-Guided Diffusion Framework

Anurag Shandilya, Akshat Gautam, Subhash C. Yadav, Shruti Bhatt et autres

Generative models have proven to be very effective in generating synthetic medical images and find applications in downstream tasks such as enhancing rare disease datasets, long-tailed dataset augmentation, and scaling machine learning algorithms. For medical applications, the synthetically generated medical images by …

One Shot GANs for Long Tail Problem in Skin Lesion Dataset using novel content space assessment metric

Kunal Deo, Deval Mehta, Kshitij S. Jadhav

Long tail problems frequently arise in the medical field, particularly due to the scarcity of medical data for rare conditions. This scarcity often leads to models overfitting on such limited samples. Consequently, when training models on datasets with heavily skewed classes, where …

Harnessing Shared Relations via Multimodal Mixup Contrastive Learning for Multimodal Classification

Raja Kumar, Raghav Singhal, Pranamya Kulkarni, Deval Mehta et autres

Deep multimodal learning has shown remarkable success by leveraging contrastive learning to capture explicit one-to-one relations across modalities. However, real-world data often exhibits shared relations beyond simple pairwise associations. We propose M3CoL, a Multimodal Mixup Contrastive Learning approach to capture nuanced shared …

Adaptive Transformer Modelling of Density Function for Nonparametric Survival Analysis

Xin Zhang, Deval Mehta, Yanan Hu, Chao Zhu et autres

Survival analysis holds a crucial role across diverse disciplines, such as economics, engineering and healthcare. It empowers researchers to analyze both time-invariant and time-varying data, encompassing phenomena like customer churn, material degradation and various medical outcomes. Given the complexity and heterogeneity of …

Revamping AI Models in Dermatology: Overcoming Critical Challenges for Enhanced Skin Lesion Diagnosis

Deval Mehta, Brigid Betz‐Stablein, Toan Dinh Nguyen, Yaniv Gal et autres

The surge in developing deep learning models for diagnosing skin lesions through image analysis is notable, yet their clinical black faces challenges. Current dermatology AI models have limitations: limited number of possible diagnostic outputs, lack of real-world testing on uncommon skin lesions, …

TPMIL: Trainable Prototype Enhanced Multiple Instance Learning for Whole Slide Image Classification

Litao Yang, Deval Mehta, Sidong Liu, Dwarikanath Mahapatra et autres

Digital pathology based on whole slide images (WSIs) plays a key role in cancer diagnosis and clinical practice. Due to the high resolution of the WSI and the unavailability of patch-level annotations, WSI classification is usually formulated as a weakly supervised problem, …