Amit Raj

Lovely Professional University

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James Hays

Georgia Institute of Technology

Varun Jampani

Google (United States)

Prabhakar Singh

University of Connecticut

Prateek Nishant

ESIC Hospital

Sudesh Kumar Arya

Government Medical College

Upasna Sinha

All India Institute of Medical Sciences

Prabhakar Singh

All India Institute of Medical Sciences

Patsorn Sangkloy

Phranakhon Rajabhat University

Abhishek Gupta

Chitwan Medical College

Jayadev Nanda

All India Institute of Medical Sciences

Cooperative Institutions

All India Institute of Medical Sciences

Banaras Hindu University

Google (United States)

All India Institute of Medical Sciences Raipur

All India Institute of Medical Sciences Bhopal

Creative Commons

Institute of Medical Sciences

Georgia Institute of Technology

Lovely Professional University

Meta (Israel)

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Beyond the basics: Advanced applications of saliva in diagnostic medicine

Baba Farid University Dental Journal (2024)

Diksha Garg Amit Raj Pranjal Ekampreet Kaur Pranav Tiwari

10.5958/2230-7273.2024.00045.3

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Tools and techniques used for the characterization of wastewater

Advances in chemical pollution, environmental management and protection (2023)

Priya Yadav Rahul Prasad Singh Pradeep Kumar Yadav Rajan Kumar Gupta Amit Raj

10.1016/bs.apmp.2023.07.002

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Deep Forward and Inverse Perceptual Models for Tracking and Prediction

Alexander Lambert Amirreza Shaban Amit Raj Zhen Liu Byron Boots

We consider the problems of learning forward models that map state to high-dimensional images and inverse models that map high-dimensional images to state in robotics. Specifically, we present a perceptual model for generating video frames from state with deep networks, and provide a framework for its use in tracking and prediction tasks. We show that our proposed model greatly outperforms standard deconvolutional methods and GANs for image generation, producing clear, photo-realistic images. We also develop a convolutional neural network model for state estimation and compare the result to an Extended Kalman Filter to estimate robot trajectories. We validate all models on a real robotic system.

Tracking (education)

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Adult-onset Dandy–Walker syndrome with atypical ocular manifestations

Canadian Journal of Ophthalmology (2023)

Prateek Nishant Naila Aftab Amit Raj Vikas Chandra Jha Upasna Sinha

Dandy–Walker syndrome

Fourth ventricle

10.1016/j.jcjo.2023.01.008

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Newer glaucoma surgeries: A review

Indian Journal of Clinical and Experimental Ophthalmology (2022)

Amit Raj Pragati Tiwari

With sophisticated diagnostic modalities used in glaucoma now-a-days, the demand for more accurate and precise intraocular pressure control is also rising. Glaucoma has seen myriad of treatment modalities including medical, laser and surgical. This article focuses on the newer glaucoma surgeries, viz-a-viz microinvasive glaucoma surgeries (MIGS) that has seen immense experimental and clinical research with favourable results. MIGS works via various mechanisms, by-passing trabecular meshwork by implanting a stent or excising the trabecular tissue, enhancing aqueous outflow through Schlemm’s canal or suprachoroidal space, and shunting the aqueous to the subconjunctival space. In this article we describe each of these procedures with the results on their efficacy and safety.

Schlemm's canal

Glaucoma surgery

Modalities

Treatment modality

10.18231/j.ijceo.2022.002

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Kernel Mean Matching for Content Addressability of GANs

arXiv (Cornell University) (2019)

Wittawat Jitkrittum Patsorn Sangkloy Muhammad Waleed Gondal Amit Raj James Hays

We propose a novel procedure which adds "content-addressability" to any given unconditional implicit model e.g., a generative adversarial network (GAN). The procedure allows users to control the generative process by specifying a set (arbitrary size) of desired examples based on which similar samples are generated from the model. The proposed approach, based on kernel mean matching, is applicable to any generative models which transform latent vectors to samples, and does not require retraining of the model. Experiments on various high-dimensional image generation problems (CelebA-HQ, LSUN bedroom, bridge, tower) show that our approach is able to generate images which are consistent with the input set, while retaining the image quality of the original model. To our knowledge, this is the first work that attempts to construct, at test time, a content-addressable generative model from a trained marginal model.

Kernel (algebra)

Generative model

Texture Synthesis

10.48550/arxiv.1905.05882

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Indigenous negative pressure wound treatment: Easy, effective and affordable

International Journal of Orthopaedics Sciences (2020)

Amit Raj Benu Gopal Das Avinash Kumar Santosh Kumar

The purpose of this study was to evaluate the use of negative pressure wound therapy in traumatic injuries, post operative wound dehiscence and pressure sores. Negative-pressure wound therapy (NPWT) is believed to accelerate wound healing by increasing blood flow, promoting angiogenesis, suction removal of matrix metalloproteinases. In our study we have used cost effective material as an alternative to the more expensive commercial NPWT in the management of complex wounds. Material and Methods: This study is a prospective evaluation of NPWT in 70 patients with traumatic injuries, post operative wound dehiscence and pressure sores. In our study we have used low cost indigenous NPWT set for wound therapy. Dressing was changed for every 4 days till the desired granulations tissue is formed for skin cover.Results: Of the 70 patients treated with indigenous NPWT, 42 (60 %) patients achieved direct closure and 28 patients (40%) required split thickness skin grafting. Conclusion: Our study showed low cost indigenous NPWT system producing comparable results at meagre cost to commercially available NPWT. NPWT has now become an established method of wound management. Low cost Indigenous NPWT can be revolutionary in wound management urban as well as in rural areas of our country.

Negative-pressure wound therapy

Wound dehiscence

Skin grafting

10.22271/ortho.2020.v6.i1e.1870

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Kernel Mean Matching for Content Addressability of GANs

arXiv (Cornell University) (2019)

Wittawat Jitkrittum Patsorn Sangkloy Muhammad Waleed Gondal Amit Raj James Hays

We propose a novel procedure which adds content-addressability to any given unconditional implicit model e.g., a generative adversarial network (GAN). The procedure allows users to control the generative process by specifying a set (arbitrary size) of desired examples based on which similar samples are generated from the model. The proposed approach, based on kernel mean matching, is applicable to any generative models which transform latent vectors to samples, and does not require retraining of the model. Experiments on various high-dimensional image generation problems (CelebA-HQ, LSUN bedroom, bridge, tower) show that our approach is able to generate images which are consistent with the input set, while retaining the image quality of the original model. To our knowledge, this is the first work that attempts to construct, at test time, a content-addressable generative model from a trained marginal model.

Kernel (algebra)

Generative model

Texture Synthesis

Source

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Citations (5)

ANR: Articulated Neural Rendering for Virtual Avatars

arXiv (Cornell University) (2020)

Amit Raj Julian Tanke James Hays Minh Vo Carsten Stoll

The combination of traditional rendering with neural networks in Deferred Neural Rendering (DNR) provides a compelling balance between computational complexity and realism of the resulting images. Using skinned meshes for rendering articulating objects is a natural extension for the DNR framework and would open it up to a plethora of applications. However, in this case the neural shading step must account for deformations that are possibly not captured in the mesh, as well as alignment inaccuracies and dynamics -- which can confound the DNR pipeline. We present Articulated Neural Rendering (ANR), a novel framework based on DNR which explicitly addresses its limitations for virtual human avatars. We show the superiority of ANR not only with respect to DNR but also with methods specialized for avatar creation and animation. In two user studies, we observe a clear preference for our avatar model and we demonstrate state-of-the-art performance on quantitative evaluation metrics. Perceptually, we observe better temporal stability, level of detail and plausibility.

Avatar

Deep Neural Networks

10.48550/arxiv.2012.12890

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Study of Relationship of Anemia among Edentulous Patients

Malaysian Journal of Medical and Biological Research (2018)

Lijo Isaac Amit Raj Reshma Karkera R Naveen Reddy

Very little studies were done on relationship of the dental status and the nutritional status. The present study was done to study relation between edentulism and the presence of anemia. The study was included of 46 adult patients with edentulism and same numbers of patients were taken as controls. The results were tabulated and analyzed with the help of IBM SPSS statistics 20 using student’s t test. The hemoglobin levels were lower in the edentulous patients that that of the control group. The present study had shown that the nutritional status were poor resulting in anemia in case of edentulous patients as compared to control group with the same age group.

Edentulism

10.18034/mjmbr.v5i2.458

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