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À propos de l'auteur
Dr. Arvind Mukundan is an accomplished researcher in hyperspectral imaging, biomedical optics, and space engineering. He holds a B.Tech. in Aerospace Engineering from SRM Institute of Science and Technology, Chennai, India, and an M.S. in Space Science and Technology with a focus on Instrumentation from Luleå University of Technology, Kiruna, Sweden, which he completed in 2018 and 2021, respectively. He went on to earn his Ph.D. in Mechanical Engineering from National Chung Cheng University, Chiayi, Taiwan, in 2023. Since September 2023, he has been advancing research as an Assistant Researcher at the Intelligent Opto-mechatronics Integration Device Center at National Chung Cheng University. Dr. Mukundan's work has garnered multiple accolades, reflecting his commitment to innovative R&D. Dr. Mukundan was identified as Stanford’s Top 2% Scientists List for the year 2023 which’s metrics focus on citations. In 2022, he received the Taiwan Comprehensive University System Young Scholars' Innovative R&D Award, a recognition bestowed by National Chung Cheng University. The following year, he was honored with the Young Researcher Award by the Institute of Scholars (InSc). His visionary research has been internationally recognized, with his project “The Dvarka Initiative” selected as a finalist in the 2019 Mars Colony Design Contest, organized by the Mars Society at the University of Southern California. Furthermore, his insight into sustainable space logistics led him to win the 2020 Sustainable Space Logistics Essay Competition at the Swiss Federal Institute of Technology Lausanne (EPFL), where he proposed frameworks for logistics essential to the sustainable future of space exploration. Dr. Mukundan's influence extends into the academic community as an editor for the special issue “New Advances in Hyperspectral and Multispectral Imaging for Disease Diagnosis” in the Biomedical Optics section of Diagnostics. He also serves as an editor for the Research Topic “Recent Trends and Advancements in Multispectral and Hyperspectral Imaging for Cancer Detection” in Frontiers in Immunology. With a robust publication portfolio of over 80 research articles, cited more than 1000 times, his work spans applications in hyperspectral imaging, color imaging, and space exploration, pushing the boundaries of scientific discovery and technological advancement. His research continues to shape future directions in medical imaging, environmental monitoring, and the potential for human settlements beyond Earth.
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Biomedical Applications in Deep Learning-Enhanced Hyperspectral Imaging
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Biomedical Applications in Deep Learning-Enhanced Hyperspectral Imaging (Paperback)
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Paperback. Etat : new. Paperback. Hyperspectral imaging (HSI) offers both spatial and spectral data across numerous contiguous wavelength bands, demonstrating unparalleled sensitivity in detecting small biochemical and morphological variations in biological tissues. The information content surpassing traditional visual imaging has generated novel potential in cancer diagnostics, histology, ophthalmology, endoscopy, and precision surgery. Despite its potential, the complete realization of HSI in medicine remains unfulfilled due to the complexity and high dimensionality of the data, obstacles posed by noise and variability, and the absence of standardized computing methodologies. Using deep learning techniques grounded in convolutional neural networks, recurrent and attention-based architectures, generative models, and multimodal fusion strategies may directly tackle these challenges. Biomedical Applications in Deep Learning-Enhanced Hyperspectral Imaging explores the nascent field at the convergence of deep learning and HSI aimed at enhancing biological science and clinical practice. It examines computational techniques, applications in oncology, ophthalmology, gastroenterology, microbiology, and pathology, and future perspectives on real-time implementation, portability, ethics, and regulatory approval. This book covers topics such as disease detection, medical technologies, and anomaly detection, and is a useful resource for medical and healthcare professionals, engineers, academicians, researchers, and scientists. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798337371849
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Biomedical Applications in Deep Learning-Enhanced Hyperspectral Imaging (Paperback)
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Paperback. Etat : new. Paperback. Hyperspectral imaging (HSI) offers both spatial and spectral data across numerous contiguous wavelength bands, demonstrating unparalleled sensitivity in detecting small biochemical and morphological variations in biological tissues. The information content surpassing traditional visual imaging has generated novel potential in cancer diagnostics, histology, ophthalmology, endoscopy, and precision surgery. Despite its potential, the complete realization of HSI in medicine remains unfulfilled due to the complexity and high dimensionality of the data, obstacles posed by noise and variability, and the absence of standardized computing methodologies. Using deep learning techniques grounded in convolutional neural networks, recurrent and attention-based architectures, generative models, and multimodal fusion strategies may directly tackle these challenges. Biomedical Applications in Deep Learning-Enhanced Hyperspectral Imaging explores the nascent field at the convergence of deep learning and HSI aimed at enhancing biological science and clinical practice. It examines computational techniques, applications in oncology, ophthalmology, gastroenterology, microbiology, and pathology, and future perspectives on real-time implementation, portability, ethics, and regulatory approval. This book covers topics such as disease detection, medical technologies, and anomaly detection, and is a useful resource for medical and healthcare professionals, engineers, academicians, researchers, and scientists. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798337371849
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Biomedical Applications in Deep Learning-Enhanced Hyperspectral Imaging (Paperback)
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Paperback. Etat : new. Paperback. Hyperspectral imaging (HSI) offers both spatial and spectral data across numerous contiguous wavelength bands, demonstrating unparalleled sensitivity in detecting small biochemical and morphological variations in biological tissues. The information content surpassing traditional visual imaging has generated novel potential in cancer diagnostics, histology, ophthalmology, endoscopy, and precision surgery. Despite its potential, the complete realization of HSI in medicine remains unfulfilled due to the complexity and high dimensionality of the data, obstacles posed by noise and variability, and the absence of standardized computing methodologies. Using deep learning techniques grounded in convolutional neural networks, recurrent and attention-based architectures, generative models, and multimodal fusion strategies may directly tackle these challenges. Biomedical Applications in Deep Learning-Enhanced Hyperspectral Imaging explores the nascent field at the convergence of deep learning and HSI aimed at enhancing biological science and clinical practice. It examines computational techniques, applications in oncology, ophthalmology, gastroenterology, microbiology, and pathology, and future perspectives on real-time implementation, portability, ethics, and regulatory approval. This book covers topics such as disease detection, medical technologies, and anomaly detection, and is a useful resource for medical and healthcare professionals, engineers, academicians, researchers, and scientists. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9798337371849
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Hyperspectral imaging (HSI) offers both spatial and spectral data across numerous contiguous wavelength bands, demonstrating unparalleled sensitivity in detecting small biochemical and morphological variations in biological tissues. The information content surpassing traditional visual imaging has generated novel potential in cancer diagnostics, histology, ophthalmology, endoscopy, and precision surgery. Despite its potential, the complete realization of HSI in medicine remains unfulfilled due to the complexity and high dimensionality of the data, obstacles posed by noise and variability, and the absence of standardized computing methodologies. Using deep learning techniques grounded in convolutional neural networks, recurrent and attention-based architectures, generative models, and multimodal fusion strategies may directly tackle these challenges. Biomedical Applications in Deep Learning-Enhanced Hyperspectral Imaging explores the nascent field at the convergence of deep learning and HSI aimed at enhancing biological science and clinical practice. It examines computational techniques, applications in oncology, ophthalmology, gastroenterology, microbiology, and pathology, and future perspectives on real-time implementation, portability, ethics, and regulatory approval. This book covers topics such as disease detection, medical technologies, and anomaly detection, and is a useful resource for medical and healthcare professionals, engineers, academicians, researchers, and scientists. N° de réf. du vendeur 9798337371849
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