Vidhiwar Singh Rathour
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Researcher
Also affiliated: Carnegie Mellon University (2021)
Unknown Researcher
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Vidhiwar Singh Rathour's research focuses on the application of deep learning techniques, particularly in the areas of computer vision and medical image analysis. His work includes developing and evaluating neural network architectures for tasks such as medical image segmentation and arrhythmia classification. Rathour has explored methods like invertible residual networks with regularization for effective volumetric segmentation and has contributed to the understanding of deep reinforcement learning in computer vision through comprehensive surveys.
His publications have addressed the classification of ECG data for arrhythmia detection using multi-module recurrent convolutional neural networks combined with transformer encoders. He has also investigated metrics for benchmarking medical image segmentation, specifically introducing the Roughness Index and Roughness Distance. Rathour collaborates with researchers at the University of Arkansas at Fayetteville, including Kashu Yamakazi and Khoa Luu, on shared publications.
Metrics
- h-index: 4
- Publications: 8
- Citations: 319
Selected Publications
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Invertible residual network with regularization for effective volumetric segmentation (2022)
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Deep reinforcement learning in computer vision: a comprehensive survey (2021)
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Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification (2021)
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Roughness Index and Roughness Distance for Benchmarking Medical Segmentation (2021)
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Roughness Index and Roughness Distance for Benchmarking Medical Segmentation (2021)
Collaboration Network
Top Collaborators
- Deep reinforcement learning in computer vision: a comprehensive survey
- Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification
- Invertible residual network with regularization for effective volumetric segmentation
- Roughness Index and Roughness Distance for Benchmarking Medical Segmentation
- Roughness Index and Roughness Distance for Benchmarking Medical Segmentation
- Roughness Index and Roughness Distance for Benchmarking Medical Segmentation
- Roughness Index and Roughness Distance for Benchmarking Medical Segmentation
- Deep reinforcement learning in computer vision: a comprehensive survey
- Invertible residual network with regularization for effective volumetric segmentation
- Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification
- Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification
- Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification
- Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification
- Deep reinforcement learning in computer vision: a comprehensive survey
- Deep reinforcement learning in computer vision: a comprehensive survey
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