Zobrazeno 1 - 10 of 539 pro vyhledávání: '"Mehta Vishal"'
1
Report
Physics Based Differentiable Rendering for Inverse Problems and Beyond
Autor: Kakkar, Preetish, Mukherjee, Srijani, Ragothaman, Hariharan, Mehta, Vishal
Publikováno v: Journal of Electrical systems, Vol. 20 No. 11s (2024)
Physics-based differentiable rendering (PBDR) has become an efficient method in computer vision, graphics, and machine learning for addressing an array of inverse problems. PBDR allows patterns to be generated from perceptions which can be applied to
Externí odkaz: http://arxiv.org/abs/2412.08563
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2
Report
Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images
Autor: Dawood, Tareen, Chen, Chen, Sidhua, Baldeep S., Ruijsink, Bram, Goulda, Justin, Porter, Bradley, Elliott, Mark K., Mehta, Vishal, Rinaldi, Christopher A., Puyol-Anton, Esther, Razavi, Reza, King, Andrew P.
Quantifying uncertainty of predictions has been identified as one way to develop more trustworthy artificial intelligence (AI) models beyond conventional reporting of performance metrics. When considering their role in a clinical decision support set
Externí odkaz: http://arxiv.org/abs/2308.15141
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3
Report
AI-enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography
Autor: Puyol-Antón, Esther, Ruijsink, Bram, Sidhu, Baldeep S., Gould, Justin, Porter, Bradley, Elliott, Mark K., Mehta, Vishal, Gu, Haotian, Xochicale, Miguel, Gomez, Alberto, Rinaldi, Christopher A., Cowie, Martin, Chowienczyk, Phil, Razavi, Reza, King, Andrew P.
Publikováno v: MICCAI ASMUS 2020
Left ventricular (LV) function is an important factor in terms of patient management, outcome, and long-term survival of patients with heart disease. The most recently published clinical guidelines for heart failure recognise that over reliance on on
Externí odkaz: http://arxiv.org/abs/2203.11726
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5
Report
Uncertainty-Aware Training for Cardiac Resynchronisation Therapy Response Prediction
Autor: Dawood, Tareen, Chen, Chen, Andlauer, Robin, Sidhu, Baldeep S., Ruijsink, Bram, Gould, Justin, Porter, Bradley, Elliott, Mark, Mehta, Vishal, Rinaldi, C. Aldo, Puyol-Antón, Esther, Razavi, Reza, King, Andrew P.
Evaluation of predictive deep learning (DL) models beyond conventional performance metrics has become increasingly important for applications in sensitive environments like healthcare. Such models might have the capability to encode and analyse large
Externí odkaz: http://arxiv.org/abs/2109.10641
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6
Report
A Multimodal Deep Learning Model for Cardiac Resynchronisation Therapy Response Prediction
Autor: Puyol-Antón, Esther, Sidhu, Baldeep S., Gould, Justin, Porter, Bradley, Elliott, Mark K., Mehta, Vishal, Rinaldi, Christopher A., King, Andrew P.
We present a novel multimodal deep learning framework for cardiac resynchronisation therapy (CRT) response prediction from 2D echocardiography and cardiac magnetic resonance (CMR) data. The proposed method first uses the `nnU-Net' segmentation model
Externí odkaz: http://arxiv.org/abs/2107.10662
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9
Report
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction
Autor: Puyol-Antón, Esther, Chen, Chen, Clough, James R., Ruijsink, Bram, Sidhu, Baldeep S., Gould, Justin, Porter, Bradley, Elliott, Mark, Mehta, Vishal, Rueckert, Daniel, Rinaldi, Christopher A., King, Andrew P.
Advances in deep learning (DL) have resulted in impressive accuracy in some medical image classification tasks, but often deep models lack interpretability. The ability of these models to explain their decisions is important for fostering clinical tr
Externí odkaz: http://arxiv.org/abs/2006.13811
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