From Fully Supervised Single Task To Semi Supervised Multi Task Deep Learning Architectures For Segmentation In Medical Imaging Applications
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From Fully Supervised Single Task To Semi Supervised Multi Task Deep Learning Architectures For Segmentation In Medical Imaging Applications
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Author : S. M. Kamrul Hasan
language : en
Publisher:
Release Date : 2023
From Fully Supervised Single Task To Semi Supervised Multi Task Deep Learning Architectures For Segmentation In Medical Imaging Applications written by S. M. Kamrul Hasan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with Deep learning (Machine learning) categories.
"Medical imaging is routinely performed in clinics worldwide for the diagnosis and treatment of numerous medical conditions in children and adults. With the advent of these medical imaging modalities, radiologists can visualize both the structure of the body as well as the tissues within the body. However, analyzing these high-dimensional (2D/3D/4D) images demands a significant amount of time and effort from radiologists. Hence, there is an ever-growing need for medical image computing tools to extract relevant information from the image data to help radiologists perform efficiently. Image analysis based on machine learning has pivotal potential to improve the entire medical imaging pipeline, providing support for clinical decision-making and computer-aided diagnosis. To be effective in addressing challenging image analysis tasks such as classification, detection, registration, and segmentation, specifically for medical imaging applications, deep learning approaches have shown significant improvement in performance. While deep learning has shown its potential in a variety of medical image analysis problems including segmentation, motion estimation, etc., generalizability is still an unsolved problem and many of these successes are achieved at the cost of a large pool of datasets. For most practical applications, getting access to a copious dataset can be very difficult, often impossible. Annotation is tedious and time-consuming. This cost is further amplified when annotation must be done by a clinical expert in medical imaging applications. Additionally, the applications of deep learning in the real-world clinical setting are still limited due to the lack of reliability caused by the limited prediction capabilities of some deep learning models. Moreover, while using a CNN in an automated image analysis pipeline, it’s critical to understand which segmentation results are problematic and require further manual examination. To this extent, the estimation of uncertainty calibration in a semi-supervised setting for medical image segmentation is still rarely reported. This thesis focuses on developing and evaluating optimized machine learning models for a variety of medical imaging applications, ranging from fully-supervised, single-task learning to semi-supervised, multi-task learning that makes efficient use of annotated training data. The contributions of this dissertation are as follows: (1) developing a fully-supervised, single-task transfer learning for the surgical instrument segmentation from laparoscopic images; and (2) utilizing supervised, single-task, transfer learning for segmenting and digitally removing the surgical instruments from endoscopic/laparoscopic videos to allow the visualization of the anatomy being obscured by the tool. The tool removal algorithms use a tool segmentation mask and either instrument-free reference frames or previous instrument-containing frames to fill in (inpaint) the instrument segmentation mask; (3) developing fully-supervised, single-task learning via efficient weight pruning and learned group convolution for accurate left ventricle (LV), right ventricle (RV) blood pool and myocardium localization and segmentation from 4D cine cardiac MR images; (4) demonstrating the use of our fully-supervised memory-efficient model to generate dynamic patient-specific right ventricle (RV) models from cine cardiac MRI dataset via an unsupervised learning-based deformable registration field; and (5) integrating a Monte Carlo dropout into our fully-supervised memory-efficient model with inherent uncertainty estimation, with the overall goal to estimate the uncertainty associated with the obtained segmentation and error, as a means to flag regions that feature less than optimal segmentation results; (6) developing semi-supervised, single-task learning via self-training (through meta pseudo-labeling) in concert with a Teacher network that instructs the Student network by generating pseudo-labels given unlabeled input data; (7) proposing largely-unsupervised, multi-task learning to demonstrate the power of a simple combination of a disentanglement block, variational autoencoder (VAE), generative adversarial network (GAN), and a conditioning layer-based reconstructor for performing two of the foremost critical tasks in medical imaging — segmentation of cardiac structures and reconstruction of the cine cardiac MR images; (8) demonstrating the use of 3D semi-supervised, multi-task learning for jointly learning multiple tasks in a single backbone module – uncertainty estimation, geometric shape generation, and cardiac anatomical structure segmentation of the left atrial cavity from 3D Gadolinium-enhanced magnetic resonance (GE-MR) images. This dissertation summarizes the impact of the contributions of our work in terms of demonstrating the adaptation and use of deep learning architectures featuring different levels of supervision to build a variety of image segmentation tools and techniques that can be used across a wide spectrum of medical image computing applications centered on facilitating and promoting the wide-spread computer-integrated diagnosis and therapy data science."--Abstract.
Supervised And Semi Supervised Multi Structure Segmentation And Landmark Detection In Dental Data
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Author : Yaqi Wang
language : en
Publisher: Springer Nature
Release Date :
Supervised And Semi Supervised Multi Structure Segmentation And Landmark Detection In Dental Data written by Yaqi Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Auto Segmentation For Radiation Oncology
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Author : Jinzhong Yang
language : en
Publisher: CRC Press
Release Date : 2021-04-18
Auto Segmentation For Radiation Oncology written by Jinzhong Yang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-18 with Science categories.
This book provides a comprehensive introduction to current state-of-the-art auto-segmentation approaches used in radiation oncology for auto-delineation of organs-of-risk for thoracic radiation treatment planning. Containing the latest, cutting edge technologies and treatments, it explores deep-learning methods, multi-atlas-based methods, and model-based methods that are currently being developed for clinical radiation oncology applications. Each chapter focuses on a specific aspect of algorithm choices and discusses the impact of the different algorithm modules to the algorithm performance as well as the implementation issues for clinical use (including data curation challenges and auto-contour evaluations). This book is an ideal guide for radiation oncology centers looking to learn more about potential auto-segmentation tools for their clinic in addition to medical physicists commissioning auto-segmentation for clinical use. Features: Up-to-date with the latest technologies in the field Edited by leading authorities in the area, with chapter contributions from subject area specialists All approaches presented in this book are validated using a standard benchmark dataset established by the Thoracic Auto-segmentation Challenge held as an event of the 2017 Annual Meeting of American Association of Physicists in Medicine
Irc Set 2022
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Author : Huaqun Guo
language : en
Publisher: Springer Nature
Release Date : 2023-05-31
Irc Set 2022 written by Huaqun Guo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-31 with Science categories.
This book highlights contemporary state of research in multi-disciplinary areas in Physics, Biomedical Sciences, Chemical Engineering, Mechanical Engineering, Computer Science/Engineering, Life Sciences, and Healthcare. The accepted submissions to the 8th IRC Conference on Science, Engineering and Technology (IRC-SET 2022) that were presented on 6th August 2022, are published in this conference proceedings. The papers presented here were shortlisted after extensive rounds of rigorous reviews by a panel of esteemed individuals who are pioneers and experts in their respective domains.
Deep Learning Applications In Medical Imaging
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Author : Saxena, Sanjay
language : en
Publisher: IGI Global
Release Date : 2020-10-16
Deep Learning Applications In Medical Imaging written by Saxena, Sanjay and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-16 with Medical categories.
Before the modern age of medicine, the chance of surviving a terminal disease such as cancer was minimal at best. After embracing the age of computer-aided medical analysis technologies, however, detecting and preventing individuals from contracting a variety of life-threatening diseases has led to a greater survival percentage and increased the development of algorithmic technologies in healthcare. Deep Learning Applications in Medical Imaging is a pivotal reference source that provides vital research on the application of generating pictorial depictions of the interior of a body for medical intervention and clinical analysis. While highlighting topics such as artificial neural networks, disease prediction, and healthcare analysis, this publication explores image acquisition and pattern recognition as well as the methods of treatment and care. This book is ideally designed for diagnosticians, medical imaging specialists, healthcare professionals, physicians, medical researchers, academicians, and students.
Machine Learning Based Adaptive Radiotherapy Treatments From Bench Top To Bedside
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Author : Jiahan Zhang
language : en
Publisher: Frontiers Media SA
Release Date : 2023-05-12
Machine Learning Based Adaptive Radiotherapy Treatments From Bench Top To Bedside written by Jiahan Zhang and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-12 with Medical categories.
Intelligent Computation And Analytics On Sustainable Energy And Environment
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Author : Amarjit Roy
language : en
Publisher: CRC Press
Release Date : 2024-11-18
Intelligent Computation And Analytics On Sustainable Energy And Environment written by Amarjit Roy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-18 with Computers categories.
The 1st International Conference on Intelligent Computation and Analytics on Sustainable Energy (ICICASEE 2023) was held at Ghani Khan Choudhury Institute of Engineering & Technology (GKCIET), Malda, West Bengal, India. GKCIET is a premier engineering institute located in Malda, West Bengal, India. Being established in 2010, at present the institute offers B.Tech and Diploma Civil Engineering, Mechanical Engineering, Electrical Engineering, Computer Science and engineering and Food process□ing technology. The conference was aimed to provide a platform for researchers, academicians, indus□try professionals, and students to exchange knowledge and ideas on intelligent computation, analytics, and their applications in sustainable energy systems. The Department of Electrical Engineering of the institute hosted the conference from September 21–23, 2023.
Machine Learning In Dentistry
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Author : Ching-Chang Ko
language : en
Publisher: Springer Nature
Release Date : 2021-07-24
Machine Learning In Dentistry written by Ching-Chang Ko and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-24 with Medical categories.
This book reviews all aspects of the use of machine learning in contemporary dentistry, clearly explaining its significance for dental imaging, oral diagnosis and treatment, dental designs, and dental research. Machine learning is an emerging field of artificial intelligence research and practice in which computer agents are employed to improve perception, cognition, and action based on their ability to “learn”, for example through use of big data techniques. Its application within dentistry is designed to promote personalized and precision patient care, with enhancement of diagnosis and treatment planning. In this book, readers will find up-to-date information on different machine learning tools and their applicability in various dental specialties. The selected examples amply illustrate the opportunities to employ a machine learning approach within dentistry while also serving to highlight the associated challenges. Machine Learning in Dentistry will be of value for all dental practitioners and researchers who wish to learn more about the potential benefits of using machine learning techniques in their work.
Medical Image Understanding And Analysis
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Author : Sharib Ali
language : en
Publisher: Springer Nature
Release Date : 2025-07-14
Medical Image Understanding And Analysis written by Sharib Ali and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-14 with Computers categories.
The three-volume set LNCS 15916,15917 & 15918 constitutes the refereed proceedings of the 29th Annual Conference on Medical Image Understanding and Analysis, MIUA 2025, held in Leeds, UK, during July 15–17, 2025. The 67 revised full papers presented in these proceedings were carefully reviewed and selected from 99 submissions. The papers are organized in the following topical sections: Part I: Frontiers in Computational Pathology; and Image Synthesis and Generative Artificial Intelligence. Part II: Image-guided Diagnosis; and Image-guided Intervention. Part III: Medical Image Segmentation; and Retinal and Vascular Image Analysis.
Deep Learning For 3d Vision Algorithms And Applications
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Author : Xiaoli Li
language : en
Publisher: World Scientific
Release Date : 2024-08-27
Deep Learning For 3d Vision Algorithms And Applications written by Xiaoli Li and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-27 with Computers categories.
3D deep learning is a rapidly evolving field that has the potential to transform various industries. This book provides a comprehensive overview of the current state-of-the-art in 3D deep learning, covering a wide range of research topics and applications. It collates the most recent research advances in 3D deep learning, including algorithms and applications, with a focus on efficient methods to tackle the key technical challenges in current 3D deep learning research and adoption, therefore making 3D deep learning more practical and feasible for real-world applications.This book is organized into five sections, each of which addresses different aspects of 3D deep learning. Section I: Sample Efficient 3D Deep Learning, focuses on developing efficient algorithms to build accurate 3D models with limited annotated samples. Section II: Representation Efficient 3D Deep Learning, deals with the challenge of developing efficient representations for dynamic 3D scenes and multiple 3D modalities. Section III: Robust 3D Deep Learning, presents methods for improving the robustness and reliability of deep learning models in real-world applications. Section IV: Resource Efficient 3D Deep Learning, explores ways to reduce the computation cost of 3D models and improve their efficiency in resource-limited environments. Section V: Emerging 3D Deep Learning Applications, showcases how 3D deep learning is transforming industries and enabling new applications for healthcare and manufacturing.This collection is a valuable resource for researchers and practitioners interested in exploring the potential of 3D deep learning.