Shuja Khalid
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View article: GaussianVAE: Adaptive Learning Dynamics of 3D Gaussians for High-Fidelity Super-Resolution
GaussianVAE: Adaptive Learning Dynamics of 3D Gaussians for High-Fidelity Super-Resolution Open
We present a novel approach for enhancing the resolution and geometric fidelity of 3D Gaussian Splatting (3DGS) beyond native training resolution. Current 3DGS methods are fundamentally limited by their input resolution, producing reconstr…
View article: Assessment of predictive value of artificial intelligence for ophthalmic diseases using electronic health records: A systematic review and meta-analysis
Assessment of predictive value of artificial intelligence for ophthalmic diseases using electronic health records: A systematic review and meta-analysis Open
Purpose: The application of artificial intelligence (AI) in ophthalmology has shown significant promise across various clinical domains. This study addresses the need for assessing the predictive value of AI models utilizing electronic hea…
View article: Proceedings of the Workshop on 3D Geometry Generation for Scientific Computing
Proceedings of the Workshop on 3D Geometry Generation for Scientific Computing Open
High-fidelity 3D geometries of the natural and built world around us are an essential part of answering some of the most pressing scientific questions of our day. Through advances in deep learning, computer vision, and artificial intellige…
View article: wildNeRF: Novel view synthesis of in-the-wild dynamic scenes using sparse monocular data
wildNeRF: Novel view synthesis of in-the-wild dynamic scenes using sparse monocular data Open
We present a novel neural radiance model that is trainable in a self-supervised manner for novel-view synthesis of dynamic unstructured scenes. Our end-to-end trainable algorithm learns highly complex, real-world static scenes within secon…
View article: Towards objective and explainable assessment of surgical skill with deep learning (Preprint)
Towards objective and explainable assessment of surgical skill with deep learning (Preprint) Open
BACKGROUND Currently, evaluating surgical technical performance is inefficient and subjective and the established rubrics for assessing surgical ability are open to interpretation. To power programs for surgical training and Maintenance o…
View article: SurGNN: Explainable visual scene understanding and assessment of surgical skill using graph neural networks
SurGNN: Explainable visual scene understanding and assessment of surgical skill using graph neural networks Open
This paper explores how graph neural networks (GNNs) can be used to enhance visual scene understanding and surgical skill assessment. By using GNNs to analyze the complex visual data of surgical procedures represented as graph structures, …
View article: wildNeRF: Novel view synthesis of in-the-wild dynamic scenes using using sparse monocular view data
wildNeRF: Novel view synthesis of in-the-wild dynamic scenes using using sparse monocular view data Open
We present a novel neural radiance model that is trainable in a selfsupervised manner for novel-view synthesis of dynamic unstructured scenes. Our end-to-end trainable algorithm learns highly complex, realworld static scenes within seconds…
View article: RefiNeRF: Modelling dynamic neural radiance fields with inconsistent or missing camera parameters
RefiNeRF: Modelling dynamic neural radiance fields with inconsistent or missing camera parameters Open
Novel view synthesis (NVS) is a challenging task in computer vision that involves synthesizing new views of a scene from a limited set of input images. Neural Radiance Fields (NeRF) have emerged as a powerful approach to address this probl…
View article: wildNeRF: Complete view synthesis of in-the-wild dynamic scenes captured using sparse monocular data
wildNeRF: Complete view synthesis of in-the-wild dynamic scenes captured using sparse monocular data Open
We present a novel neural radiance model that is trainable in a self-supervised manner for novel-view synthesis of dynamic unstructured scenes. Our end-to-end trainable algorithm learns highly complex, real-world static scenes within secon…
View article: OR Vision: Objective, explainable assessment of surgical skill with deep learning
OR Vision: Objective, explainable assessment of surgical skill with deep learning Open
Background Currently, evaluating surgical technical performance is inefficient and subjective [1,2,3,4] and the established rubrics for assessing surgical ability are open to interpretation. To power programs for surgical training and Main…
View article: Advances in Prediction of Readmission Rates Using Long Term Short Term Memory Networks on Healthcare Insurance Data
Advances in Prediction of Readmission Rates Using Long Term Short Term Memory Networks on Healthcare Insurance Data Open
30-day hospital readmission is a long standing medical problem that affects patients' morbidity and mortality and costs billions of dollars annually. Recently, machine learning models have been created to predict risk of inpatient readmiss…
View article: Evaluation of Deep Learning Models for Identifying Surgical Actions and Measuring Performance
Evaluation of Deep Learning Models for Identifying Surgical Actions and Measuring Performance Open
The proposed models and the accompanying results illustrate that deep machine learning can identify associations in surgical video clips. These are the first steps to creating a feedback mechanism for surgeons that would allow them to lear…
View article: Player Availability Rating (PAR) - A Tool for Quantifying Skater Performance for NHL General Managers
Player Availability Rating (PAR) - A Tool for Quantifying Skater Performance for NHL General Managers Open
This project aims to assess the performance of various regression models in predicting the performance of hockey players. The measure of performance is chosen to be points scored (sum of goals scored and assists made) by individual players…