Ibsa Jalata
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Researcher
Formerly Arkansas Affiliated with University of Arkansas through 2024; recent publications list Cambridge Quantum Computing (United Kingdom), Computing Center.
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Ibsa Jalata's research focuses on the application of machine learning and deep learning techniques, particularly neural networks, to medical imaging and computer vision tasks. His work includes developing algorithms for image reconstruction, segmentation, and domain adaptation. Jalata has investigated unsupervised deep learning architectures for accelerated magnetic resonance imaging (MRI) and explored semi-supervised methods for medical image segmentation using augmentation techniques like Puzzlemix. He also studies the use of graph convolutional neural networks for movement analysis in neurological and musculoskeletal disorders and the application of non-volume preserving-based fusion for group-level emotion recognition in crowd videos. Jalata has authored 11 publications, accumulating 78 citations, and holds an h-index of 5. He collaborates with researchers at the University of Arkansas at Fayetteville, including Ukash Nakarmi, Ky Luu, and Thanh-Dat Truong.
Metrics
- h-index: 5
- Publications: 11
- Citations: 78
Selected Publications
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Semi-Supervised Medical Image Segmentation using Puzzlemix Augmentation Technique (2024)
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Cut-Puzzle mix: Scribble Guided Medical Image Segmentation without Segmentation Masks (2024)
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Learning From Oversampling: A Systematic Exploitation of Oversampling to Address Data Scarcity Issues in Deep Learning- Based Magnetic Resonance Image Reconstruction (2024)
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When System Model Meets Image Prior: An Unsupervised Deep Learning Architecture for Accelerated Magnetic Resonance Imaging (2023)
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EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring (2022)
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Non-volume preserving-based fusion to group-level emotion recognition on crowd videos (2022)
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Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network (2021)
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MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices (2019)
Collaboration Network
Top Collaborators
- Learning From Oversampling: A Systematic Exploitation of Oversampling to Address Data Scarcity Issues in Deep Learning- Based Magnetic Resonance Image Reconstruction
- When System Model Meets Image Prior: An Unsupervised Deep Learning Architecture for Accelerated Magnetic Resonance Imaging
- Cut-Puzzle mix: Scribble Guided Medical Image Segmentation without Segmentation Masks
- Semi-Supervised Medical Image Segmentation using Puzzlemix Augmentation Technique
- Non-volume preserving-based fusion to group-level emotion recognition on crowd videos
- Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- Non-volume preserving-based fusion to group-level emotion recognition on crowd videos
- Non-volume preserving-based fusion to group-level emotion recognition on crowd videos
- Non-volume preserving-based fusion to group-level emotion recognition on crowd videos
- Non-volume preserving-based fusion to group-level emotion recognition on crowd videos
- Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network
- Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- Learning From Oversampling: A Systematic Exploitation of Oversampling to Address Data Scarcity Issues in Deep Learning- Based Magnetic Resonance Image Reconstruction
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