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Author: James Boit
Publisher:
ISBN:
Category : Diagnostic imaging
Languages : en
Pages : 238
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Book Description
"Deep neural networks have revolutionized the performances of many machine learning tasks such as medical image classification and segmentation. ... However, training these deep neural networks requires high computational resources and very large amounts of labeled data which is often expensive and laborious. Meanwhile, recent studies have shown the transfer learning (TL) paradigm as an attractive choice in providing promising solutions to challenges of shortage in the availability of labeled medical images. Accordingly, TL enables us to leverage the knowledge learned from related data to solve a new problem. The objective of this dissertation is to examine the effectiveness of TL systems on medical images." -Abstract, leaf iv
Author: James Boit
Publisher:
ISBN:
Category : Diagnostic imaging
Languages : en
Pages : 238
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Book Description
"Deep neural networks have revolutionized the performances of many machine learning tasks such as medical image classification and segmentation. ... However, training these deep neural networks requires high computational resources and very large amounts of labeled data which is often expensive and laborious. Meanwhile, recent studies have shown the transfer learning (TL) paradigm as an attractive choice in providing promising solutions to challenges of shortage in the availability of labeled medical images. Accordingly, TL enables us to leverage the knowledge learned from related data to solve a new problem. The objective of this dissertation is to examine the effectiveness of TL systems on medical images." -Abstract, leaf iv
Author: Saban Ozturk
Publisher: CRC Press
ISBN: 1000818020
Category : Science
Languages : en
Pages : 275
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Book Description
The rise in living standards increases the expectation of people in almost every field. At the forefront is health. Over the past few centuries, there have been major developments in healthcare. Medical device technology and developments in artificial intelligence (AI) are among the most important ones. The improving technology and our ability to harness the technology effectively by means such as AI have led to unprecedented advances, resulting in early diagnosis of diseases. AI algorithms enable the fast and early evaluation of images from medical devices to maximize the benefits. While developments in the field of AI were quickly adapted to the field of health, in some cases this contributed to the formation of innovative artificial intelligence algorithms. Today, the most effective artificial intelligence method is accepted as deep learning. Convolutional neural network (CNN) architectures are deep learning algorithms used for image processing. This book contains applications of CNN methods. The content is quite extensive, including the application of different CNN methods to various medical image processing problems. Readers will be able to analyze the effects of CNN methods presented in the book in medical applications.
Author: Gobert Lee
Publisher: Springer Nature
ISBN: 3030331288
Category : Medical
Languages : en
Pages : 184
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Book Description
This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.
Author: S. Kevin Zhou
Publisher: Academic Press
ISBN: 0323858880
Category : Computers
Languages : en
Pages : 544
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Book Description
Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis.· Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache
Author: M. F. Mridha
Publisher: Springer Nature
ISBN: 9819739667
Category :
Languages : en
Pages : 277
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Book Description
Author: Shui-Hua Wang
Publisher: Springer
ISBN: 9811040265
Category : Computers
Languages : en
Pages : 237
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Book Description
This book provides detailed practical guidelines on how to develop an efficient pathological brain detection system, reflecting the latest advances in the computer-aided diagnosis of structural magnetic resonance brain images. Matlab codes are provided for most of the functions described. In addition, the book equips readers to easily develop the pathological brain detection system further on their own and apply the technologies to other research fields, such as Alzheimer’s detection, multiple sclerosis detection, etc.
Author: Kruttika Sutrave
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
"Deep learning has driven AI's rapid growth in recent years, especially in the medical domain, where deep CNNs are the state-of-the-art for image recognition and classification. However, training them from scratch is challenging due to the lack of data and high computational requirements. Transfer Learning (TL) is an effective approach for limited training data, and TL integrated with GANs has improved image analysis models. It was unclear how much impact big data-driven Deep Learning systems had on adoption and acceptance in real-world healthcare. Specifically, the effectiveness of recently developed DL systems as scalable and generalizable AI applications remained an open question. Accordingly, the main objective of this research work is to assess the effectiveness of TL-GAN systems on broad adoption. This study explored the combination of transfer learning and generative adversarial networks (GANs) in medical imaging by conducting a systematic literature review. In addition, the scalability dimension of these systems was evaluated by examining the dynamics of GAN-augmented datasets and the accuracy achieved on target datasets. Finally, the generalization capabilities of the combination of transfer learning and GANs were evaluated. The study added to the current literature on TL and GANs in medical imaging, specifically in image synthesis and computational efficiency. Two strategies for combining TL and GANs were identified and summarized. The study also examined the impact of artificially augmented training datasets on the Fine-Tuning layer, finding that larger datasets resulted in more parameters being trained for optimal performance. Additionally, the study investigated the effect of synthetic dataset size on classification accuracy in TL settings, concluding that target validation accuracy stabilized as the dataset size increased. Furthermore, the study explored the generalizability of the models trained on GAN-augmented datasets and found that pre-trained models exhibited good performance when applied to various target datasets, indicating a high degree of generalizability in the models." -- Abstract (leaves iv)
Author: Ripon Patgiri
Publisher: Springer Nature
ISBN: 9811597359
Category : Technology & Engineering
Languages : en
Pages : 384
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Book Description
This book presents innovative research works to demonstrate the potential and the advancements of computing approaches to utilize healthcare centric and medical datasets in solving complex healthcare problems. Computing technique is one of the key technologies that are being currently used to perform medical diagnostics in the healthcare domain, thanks to the abundance of medical data being generated and collected. Nowadays, medical data is available in many different forms like MRI images, CT scan images, EHR data, test reports, histopathological data and doctor patient conversation data. This opens up huge opportunities for the application of computing techniques, to derive data-driven models that can be of very high utility, in terms of providing effective treatment to patients. Moreover, machine learning algorithms can uncover hidden patterns and relationships present in medical datasets, which are too complex to uncover, if a data-driven approach is not taken. With the help of computing systems, today, it is possible for researchers to predict an accurate medical diagnosis for new patients, using models built from previous patient data. Apart from automatic diagnostic tasks, computing techniques have also been applied in the process of drug discovery, by which a lot of time and money can be saved. Utilization of genomic data using various computing techniques is another emerging area, which may in fact be the key to fulfilling the dream of personalized medications. Medical prognostics is another area in which machine learning has shown great promise recently, where automatic prognostic models are being built that can predict the progress of the disease, as well as can suggest the potential treatment paths to get ahead of the disease progression.
Author: Rajesh Godasu
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
"Transfer learning has emerged as a pivotal technique in deep learning, allowing pre-trained models to be fine-tuned for novel tasks. This has often led to enhanced performance and reduced training time. Lightweight architectures known for their efficiency and speed, without compromising accuracy complement this technique. These two techniques combined address the issues of limited availability of training data and the requirement of high computational resources. Numerous researchers delved into these methods to address the challenges posed by the Covid-19 pandemic. Within this framework, our study sought to evaluate the multi-stage transfer learning method across several dataset sizes and truncated versions of the lightweight MobileNetV2 architecture for medical image classification. Initially, an exhaustive literature review was conducted to present the latest advancements in transfer learning and lightweight architecture applications for COVID-19 image classification. Additionally, this study explores the trade-offs between finetuning and dataset size in a multi-stage transfer learning system using a lightweight architecture. Lastly, in this study, we aim to analyze the performance of truncated lightweight models against different training dataset sizes in a multi-stage transfer learning framework. The results of this study significantly enhance the knowledge in this specialized research area. The most popular lightweight architectures were identified, and we found that standard CNNs can be truncated without losing performance. The study also established that mid-sized datasets and freezing 90 to 95 layers yield the best performance on the target task. Furthermore, the study highlights a key trade-off in stage one transfer learning: smaller datasets require more extensive layer training, while larger datasets need fewer re-trained layers for optimal performance. Also, beyond a certain dataset size, deeper fine-tuning does not lead to improved accuracy. Finally, we established that complex models tolerate more frozen layers, maintaining adequate learning capacity. The findings support the idea that while more complex models demonstrate higher accuracy, simpler models perform competitively. Moreover, the impact of model complexity is reduced on target performance with small-size datasets. These insights underscore the efficiency of multi-stage transfer learning with lightweight models, especially when pre-trained models are applied to new datasets. The present research adds to the improvement of the effectiveness of medical expert systems while also reducing the burden on healthcare professionals." -- Abstract (leaf iv)
Author: Ngangbam Herojit Singh
Publisher: CRC Press
ISBN: 1040107117
Category : Computers
Languages : en
Pages : 274
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Book Description
This book offers detailed information on biomedical imaging using Deep Convolutional Neural Networks (Deep CNN). It focuses on different types of biomedical images to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis and image processing perspectives. Deep Learning in Biomedical Signal and Medical Imaging discusses classification, segmentation, detection, tracking, and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT, and X-RAY, amongst others. It surveys the most recent techniques and approaches in this field, with both broad coverage and enough depth to be of practical use to working professionals. It includes examples of the application of signal and image processing employing Deep CNN to Alzheimer’s, brain tumor, skin cancer, breast cancer, and stroke prediction, as well as ECG and EEG signals. This book offers enough fundamental and technical information on these techniques, approaches, and related problems without overcrowding the reader’s head. It presents the results of the latest investigations in the field of Deep CNN for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine the fundamental theory of artificial intelligence (AI), machine learning (ML,) and Deep CNN with practical applications in biology and medicine. Certainly, the list of topics covered in this book is not exhaustive, but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis. The book is written for graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and computer science. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educators who are working in the context of the topics.
