Publications by authors named "Sheheryar Khan"

Introduction: Sepsis is a serious condition that often results in high fatality rates, particularly in intensive care units (ICUs). Its nonspecific clinical characteristics makes early diagnosis and therapy difficult, despite how critical they are. The use of biomarkers like procalcitonin (PCT) and C-reactive protein (CRP) in the diagnosis and prognosis of sepsis has demonstrated encouraging results.

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Recently attention-based networks have been successful for image restoration tasks. However, existing methods are either computationally expensive or have limited receptive fields, adding constraints to the model. They are also less resilient in spatial and contextual aspects and lack pixel-to-pixel correspondence, which may degrade feature representations.

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We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, including the percentage of full-thickness cartilage loss (FCL), mean thickness, surface area, and volume. CartiMorph leverages the power of deep learning models for hierarchical image feature representation.

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This paper presents a cross-modality generative learning framework for transitive magnetic resonance imaging (MRI) from electrical impedance tomography (EIT). The proposed framework is aimed at converting low-resolution EIT images to high-resolution wrist MRI images using a cascaded cycle generative adversarial network (CycleGAN) model. This model comprises three main components: the collection of initial EIT from the medical device, the generation of a high-resolution transitive EIT image from the corresponding MRI image for domain adaptation, and the coalescence of two CycleGAN models for cross-modality generation.

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The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning.

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Background And Objective: Precise segmentation of knee tissues from magnetic resonance imaging (MRI) is critical in quantitative imaging and diagnosis. Convolutional neural networks (CNNs), being state of the art, often challenged by the lack of image-specific adaptation, such as low tissue contrasts and structural inhomogeneities, thereby leading to incomplete segmentation results.

Methods: This paper presents a deep learning-based automatic segmentation framework for precise knee tissue segmentation.

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Background: Childhood obesity is a global epidemic and is associated with a higher risk of chronic diseases such as hypertension, diabetes mellitus and other metabolic disorders. Several adipokines including resistin, visfatin, leptin and adiponectin are synthesized and secreted by adipocytes, which play an important role in obesity.

Patients & Methods: A total of 90 subjects (60 controls and 30 obese) between the ages of 5 and 18 years were selected.

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