Download or read book Advanced Computational Methods for Oncological Image Analysis written by Leonardo Rundo and published by Mdpi AG. This book was released on 2021-12-06 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians' unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations-such as segmentation, co-registration, classification, and dimensionality reduction-and multi-omics data integration.
Download or read book Medical Imaging Systems Technology Analysis and computational methods written by Cornelius T. Leondes and published by World Scientific Publishing Company Incorporated. This book was released on 2005 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ch. 1. Modeling for medical image analysis : framework and applications / Marek Kretowski and Johanne Bézy-Wendling -- ch. 2. Biomechanical models for image analysis and simulation / M. Sermesant, H. Delingette and N. Ayache -- ch. 3. Techniques in fractal analysis and their applications in brain MRI / Khan M. Iftekharuddin -- ch. 4. Techniques in infrared microspectroscopy and advanced computational methods for colon cancer diagnosis / S. Mordechai ... [et al.] -- ch. 5. Advances in computerized image analysis methods on breast ultrasound / Anant Madabhushi and Dimitris N. Metaxas -- ch. 6. Techniques in blind deblurring of spiral computed tomography images and their applications / Ming Jiang and Jing Wang -- ch. 7. Model-based 3D encoding/2D decoding of medical imaging data / G. Menegaz -- ch. 8. Interpolation techniques in multimodality image registration and their application / Jeffrey Tsao, Jim Xiuquan Ji and Zhi-Pei Liang -- ch. 9. Automatic construction of cardiac statistical shape models : applications in SPECT and MR imaging / Sebastián Ordás and Alejandro F. Frangi -- ch. 10. Techniques for mutual information-based brain image. Registration and their applications / Hua-Mei Chen and Pramod K. Varshney -- ch. 11. Iterative algebraic algorithms for image reconstruction / Ming Jiang
Download or read book Advanced Computational Methods in Multi view Medical Imaging written by Liron Yatziv and published by . This book was released on 2012 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computational Systems Biology Approaches in Cancer Research written by Inna Kuperstein and published by CRC Press. This book was released on 2019-09-09 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for Computational Systems BiologyApproaches in Cancer Research: "Complex concepts are written clearly and with informative illustrations and useful links. The book is enjoyable to read yet provides sufficient depth to serve as a valuable resource for both students and faculty." — Trey Ideker, Professor of Medicine, UC Xan Diego, School of Medicine "This volume is attractive because it addresses important and timely topics for research and teaching on computational methods in cancer research. It covers a broad variety of approaches, exposes recent innovations in computational methods, and provides acces to source code and to dedicated interactive web sites." — Yves Moreau, Department of Electrical Engineering, SysBioSys Centre for Computational Systems Biology, University of Leuven With the availability of massive amounts of data in biology, the need for advanced computational tools and techniques is becoming increasingly important and key in understanding biology in disease and healthy states. This book focuses on computational systems biology approaches, with a particular lens on tackling one of the most challenging diseases - cancer. The book provides an important reference and teaching material in the field of computational biology in general and cancer systems biology in particular. The book presents a list of modern approaches in systems biology with application to cancer research and beyond. It is structured in a didactic form such that the idea of each approach can easily be grasped from the short text and self-explanatory figures. The coverage of topics is diverse: from pathway resources, through methods for data analysis and single data analysis to drug response predictors, classifiers and image analysis using machine learning and artificial intelligence approaches. Features Up to date using a wide range of approaches Applicationexample in each chapter Online resources with useful applications’
Download or read book Medical Imaging Systems Technology written by Cornelius T. Leondes and published by . This book was released on 2005* with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computational Biology written by Tuan Pham and published by Springer Science & Business Media. This book was released on 2009-09-23 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume covers techniques in computational biology and their applications in oncology. It details advanced statistical methods, heuristic algorithms, cluster analysis, data modeling, and image and pattern analysis applied to cancer research.
Download or read book Computational Intelligence in Cancer Diagnosis written by Janmenjoy Nayak and published by Academic Press. This book was released on 2023-04-12 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Intelligence in Cancer Diagnosis: Progress and Challenges provides insights into the current strength and weaknesses of different applications and research findings on computational intelligence in cancer research. The book improves the exchange of ideas and coherence among various computational intelligence methods and enhances the relevance and exploitation of application areas for both experienced and novice end-users. Topics discussed include neural networks, fuzzy logic, connectionist systems, genetic algorithms, evolutionary computation, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. The book's chapters are written by international experts from both cancer research, oncology and computational sides to cover different aspects and make it comprehensible for readers with no background on informatics. - Contains updated information about advanced computational intelligence, spanning the areas of neural networks, fuzzy logic, connectionist systems, genetic algorithms, evolutionary computation, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems in diagnosing cancer diseases - Discusses several cancer types, including their detection, treatment and prevention - Presents case studies that illustrate the applications of intelligent computing in data analysis to help readers to analyze and advance their research in cancer
Download or read book Computational Methods in Molecular Imaging Technologies written by Vinit Kumar Gunjan and published by Springer. This book was released on 2017-07-04 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights the experimental investigations that have been carried out on magnetic resonance imaging and computed tomography (MRI & CT) images using state-of-the-art Computational Image processing techniques, and tabulates the statistical values wherever necessary. In a very simple and straightforward way, it explains how image processing methods are used to improve the quality of medical images and facilitate analysis. It offers a valuable resource for researchers, engineers, medical doctors and bioinformatics experts alike.
Download or read book Computational Molecular Magnetic Resonance Imaging for Neuro oncology written by Michael O. Dada and published by Springer Nature. This book was released on 2021-07-31 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on the analytical methods and the computer programs presented in this book, all that may be needed to perform MRI tissue diagnosis is the availability of relaxometric data and simple computer program proficiency. These programs are easy to use, highly interactive and the data processing is fast and unambiguous. Laboratories (with or without sophisticated facilities) can perform computational magnetic resonance diagnosis with only T1 and T2 relaxation data. The results have motivated the use of data to produce data-driven predictions required for machine learning, artificial intelligence (AI) and deep learning for multidisciplinary and interdisciplinary research. Consequently, this book is intended to be very useful for students, scientists, engineers, the medical personnel and researchers who are interested in developing new concepts for deeper appreciation of computational magnetic resonance imaging for medical diagnosis, prognosis, therapy and management of tissue diseases.
Download or read book Advanced Machine Learning Methods for Oncological Image Analysis written by Mehdi Astaraki and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Advances in Deep Learning for Medical Image Analysis written by Archana Mire and published by CRC Press. This book was released on 2022-04-26 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases. The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimer’s disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.
Download or read book Principles And Advanced Methods In Medical Imaging And Image Analysis written by Atam P Dhawan and published by World Scientific. This book was released on 2008-03-17 with total page 869 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computerized medical imaging and image analysis have been the central focus in diagnostic radiology. They provide revolutionalizing tools for the visualization of physiology as well as the understanding and quantitative measurement of physiological parameters. This book offers in-depth knowledge of medical imaging instrumentation and techniques as well as multidimensional image analysis and classification methods for research, education, and applications in computer-aided diagnostic radiology. Internationally renowned researchers and experts in their respective areas provide detailed descriptions of the basic foundation as well as the most recent developments in medical imaging, thus helping readers to understand theoretical and advanced concepts for important research and clinical applications.
Download or read book Biomedical Computing for Breast Cancer Detection and Diagnosis written by Pinheiro dos Santos, Wellington and published by IGI Global. This book was released on 2020-07-17 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: Despite success with treatment when diagnosed early, breast cancer is still one of the most fatal forms of cancer for women. Imaging diagnosis is still one of the most efficient ways to detect early breast changes with mammography among the most used techniques. However, there are other techniques that have emerged as alternatives or even complementary tests in the early detection of breast lesions (e.g., breast thermography and electrical impedance tomography). Artificial intelligence can be used to optimize image diagnosis, increasing the reliability of the reports and supporting professionals who do not have enough knowledge or experience to make good diagnoses. Biomedical Computing for Breast Cancer Detection and Diagnosis is a collection of research that presents a review of the physiology and anatomy of the breast; the dynamics of breast cancer; principles of pattern recognition, artificial neural networks, and computer graphics; and the breast imaging techniques and computational methods to support and optimize the diagnosis. While highlighting topics including mammograms, thermographic imaging, and intelligent systems, this book is ideally designed for medical oncologists, surgeons, biomedical engineers, medical imaging professionals, cancer researchers, academicians, and students in medicine, biomedicine, biomedical engineering, and computer science.
Download or read book Machine and Deep Learning in Oncology Medical Physics and Radiology written by Issam El Naqa and published by Springer Nature. This book was released on 2022-02-02 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
Download or read book Radiomics and Radiogenomics in Neuro Oncology written by Sanjay Saxena and published by Elsevier. This book was released on 2024-03-29 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neuro-oncology broadly encompasses life-threatening brain and spinal cord malignancies, including primary lesions and lesions metastasizing to the central nervous system. It is well suited for diagnosis, classification, and prognosis as well as assessing treatment response. Radiomics and Radiogenomics (R-n-R) have become two central pillars in precision medicine for neuro-oncology.Radiomics is an approach to medical imaging used to extract many quantitative imaging features using different data characterization algorithms, while Radiogenomics, which has recently emerged as a novel mechanism in neuro-oncology research, focuses on the relationship of imaging phenotype and genetics of cancer. Due to the exponential progress of different computational algorithms, AI methods are composed to advance the precision of diagnostic and therapeutic approaches in neuro-oncology.The field of radiomics has been and definitely will remain at the lead of this emerging discipline due to its efficiency in the field of neuro-oncology. Several AI approaches applied to conventional and advanced medical imaging data from the perspective of radiomics are very efficient for tasks such as survival prediction, heterogeneity analysis of cancer, pseudo progression analysis, and infiltrating tumors. Radiogenomics advances our understanding and knowledge of cancer biology, letting noninvasive sampling of the molecular atmosphere with high spatial resolution along with a systems-level understanding of causal heterogeneous molecular and cellular processes. These AI-based R-n-R tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and permit better dynamic treatment monitoring in this period of personalized medicine. While extremely promising, the clinical acceptance of R-n-R methods and approaches will primarily hinge on their resilience to non-standardization across imaging protocols and their capability to show reproducibility across large multi-institutional cohorts.Radiomics and Radiogenomics in Neuro-Oncology: An Artificial Intelligence Paradigm provides readers with a broad and detailed framework for R-n-R approaches with AI in neuro-oncology, the description of cancer biology and genomics study of cancer, and the methods usually implemented for analyzing. Readers will also learn about the current solutions R-n-R can offer for personalized treatments of patients, limitations, and prospects. There is comprehensive coverage of information based on radiomics, radiogenomics, cancer biology, and medical image analysis viewpoints on neuro-oncology, so this in-depth coverage is divided into two Volumes.Volume 1: Radiogenomics Flow Using Artificial Intelligence provides coverage of genomics and molecular study of brain cancer, medical imaging modalities and analysis in neuro-oncology, and prognostic and predictive models using radiomics.Volume 2: Genetics and Clinical Applications provides coverage of imaging signatures for brain cancer molecular characteristics, clinical applications of R-n-R in neuro-oncology, and Machine Learning and Deep Learning AI approaches for R-n-R in neuro-oncology. - Includes coverage on the foundational concepts of the emerging fields of radiomics and radiogenomics - Covers neural engineering modeling and AI algorithms for the imaging, diagnosis, and predictive modeling of neuro-oncology - Presents crucial technologies and software platforms, along with advanced brain imaging techniques such as quantitative imaging using CT, PET, and MRI - Provides in-depth technical coverage of computational modeling techniques and applied mathematics for brain tumor segmentation and radiomics features such as extraction and selection
Download or read book Novel Methods for Oncologic Imaging Analysis Radiomics Machine Learning and Artificial Intelligence written by Xuelei Ma and published by Frontiers Media SA. This book was released on 2021-09-23 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computational Imaging and Analytics in Biomedical Engineering written by T. R. Ganesh Babu and published by CRC Press. This book was released on 2024-07-26 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new book focuses on mathematical and numerical methods for medical images and data. The book presents the various mathematical modeling techniques, numerical analysis, computing and computational techniques, and applications of machine learning for medical images and medical informatics. It also focuses on programming concepts using MATLAB and Phython for medical image and signal analytics. The volume demonstrates the use of computational techniques and tools such as machine learning, deep neural networks, artificial intelligence and human-computer interaction ,fusion methods for CT and pet images, etc., for diagnosis of brain disorders, cervical cancer, lung disease, melanoma, atrial fibrillation and other circulatory issues, dental images, diabetes, and other medical issues.
