Artificial Intelligence Techniques in Breast Cancer Diagnosis and Prognosis

Artificial Intelligence Techniques in Breast Cancer Diagnosis and Prognosis
Author: Ashlesha Jain,Ajita Jain,Sandhya Jain
Publsiher: World Scientific
Total Pages: 330
Release: 2000
Genre: Computers
ISBN: 9789810243746

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The main aim of this book is to present a sample of recent research on the application of novel artificial intelligence paradigms to the diagnosis and prognosis of breast cancer. These paradigms include neural networks, fuzzy logic and evolutionary computing. Artificial intelligence techniques offer advantages ? such as adaptation, fault tolerance, learning and human-like behavior ? over conventional computing techniques. The idea is to combine the pathological, intelligent and statistical approaches to enable simple and accurate diagnosis and prognosis.This book is the first of its kind on the topic of artificial intelligence in breast cancer. It presents the applications of artificial intelligence in breast cancer diagnosis and prognosis, and includes state-of-the-art concepts in the field. It contains contributions from Australia, Germany, Italy, UK and the USA.

Artificial Intelligence Approach to Breast Cancer Classification

Artificial Intelligence Approach to Breast Cancer Classification
Author: Priyanka Vaidya
Publsiher: Unknown
Total Pages: 107
Release: 2009
Genre: Bioinformatics
ISBN: OCLC:428948496

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"Breast cancer is the second most common form of cancer amongst females and also the fifth most cause of cancer deaths worthwide. In case of this particular type of malignancy, early detection is the best form of cure and hence timely and accurate diagnosis of the tumor is extremely vital. Extensive research has been carried out on automating the critical diagnosis procedure as various machine learning algorithms and software tools have been deployed to aid physicians in optimizing the decision task effectively. In this research, we present a novel matrix of an artificial neural network system to effectively classify breast cancer tumors as either malignant or benign. This classification system makes use of both clinical as well as genetic data. Artificial neural networks of different architectures are incorporated in the system to classify both the image based clinical dataset as well as the microarray dataset derived from blood cells. Both the datasets are subjected to indificual analysis to compute the optimum number of input features to the neural network matrix. Randomly selected sample instances from both the clinical and microarray original datasets then serve as an input to the Dempster-Shafer theory of evidence block where the outputs are fused to provide a final diagnostic assessment and compared with the neural network analysis. The results indicate that the fused output of the Dempster-Shafer block significantly outperform the individual classifier's outputs."--Abstract.

Artificial Intelligence and Machine Learning for Digital Pathology

Artificial Intelligence and Machine Learning for Digital Pathology
Author: Andreas Holzinger,Randy Goebel,Michael Mengel,Heimo Müller
Publsiher: Springer Nature
Total Pages: 341
Release: 2020-06-24
Genre: Computers
ISBN: 9783030504021

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Data driven Artificial Intelligence (AI) and Machine Learning (ML) in digital pathology, radiology, and dermatology is very promising. In specific cases, for example, Deep Learning (DL), even exceeding human performance. However, in the context of medicine it is important for a human expert to verify the outcome. Consequently, there is a need for transparency and re-traceability of state-of-the-art solutions to make them usable for ethical responsible medical decision support. Moreover, big data is required for training, covering a wide spectrum of a variety of human diseases in different organ systems. These data sets must meet top-quality and regulatory criteria and must be well annotated for ML at patient-, sample-, and image-level. Here biobanks play a central and future role in providing large collections of high-quality, well-annotated samples and data. The main challenges are finding biobanks containing ‘‘fit-for-purpose’’ samples, providing quality related meta-data, gaining access to standardized medical data and annotations, and mass scanning of whole slides including efficient data management solutions.

Artificial Intelligence in Medicine

Artificial Intelligence in Medicine
Author: David Riaño,Szymon Wilk,Annette ten Teije
Publsiher: Springer
Total Pages: 429
Release: 2019-06-19
Genre: Computers
ISBN: 9783030216429

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This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.

Digital Breast Tomosynthesis

Digital Breast Tomosynthesis
Author: Alberto Tagliafico,Nehmat Houssami,Massimo Calabrese
Publsiher: Springer
Total Pages: 148
Release: 2016-05-03
Genre: Medical
ISBN: 9783319286310

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This book provides a comprehensive description of the screening and clinical applications of digital breast tomosynthesis (DBT) and offers straightforward, clear guidance on use of the technique. Informative clinical cases are presented to illustrate how to take advantage of DBT in clinical practice. The importance of DBT as a diagnostic tool for both screening and diagnosis is increasing rapidly. DBT improves upon mammography by depicting breast tissue on a video clip made of cross‐sectional images reconstructed in correspondence with their mammographic planes of acquisition. DBT results in markedly reduced summation of overlapping breast tissue and offers the potential to improve mammographic breast cancer surveillance and diagnosis. This book will be an excellent practical teaching guide for beginners and a useful reference for more experienced radiologists.

Artificial Intelligence and Deep Learning in Pathology

Artificial Intelligence and Deep Learning in Pathology
Author: Stanley Cohen
Publsiher: Elsevier Health Sciences
Total Pages: 288
Release: 2020-06-02
Genre: Medical
ISBN: 9780323675376

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Recent advances in computational algorithms, along with the advent of whole slide imaging as a platform for embedding artificial intelligence (AI), are transforming pattern recognition and image interpretation for diagnosis and prognosis. Yet most pathologists have just a passing knowledge of data mining, machine learning, and AI, and little exposure to the vast potential of these powerful new tools for medicine in general and pathology in particular. In Artificial Intelligence and Deep Learning in Pathology, Dr. Stanley Cohen covers the nuts and bolts of all aspects of machine learning, up to and including AI, bringing familiarity and understanding to pathologists at all levels of experience. Focuses heavily on applications in medicine, especially pathology, making unfamiliar material accessible and avoiding complex mathematics whenever possible. Covers digital pathology as a platform for primary diagnosis and augmentation via deep learning, whole slide imaging for 2D and 3D analysis, and general principles of image analysis and deep learning. Discusses and explains recent accomplishments such as algorithms used to diagnose skin cancer from photographs, AI-based platforms developed to identify lesions of the retina, using computer vision to interpret electrocardiograms, identifying mitoses in cancer using learning algorithms vs. signal processing algorithms, and many more.

Breast Imaging

Breast Imaging
Author: Christoph I. Lee,Lawrence W. Bassett,Constance D. Lehman
Publsiher: Oxford University Press
Total Pages: 536
Release: 2018
Genre: Medical
ISBN: 9780190270261

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Breast Imaging presents a comprehensive review of the subject matter commonly encountered by practicing radiologists and radiology residents in training. This volume includes succinct overviews of breast cancer epidemiology, screening, staging, and treatment; overviews of all imaging modalities including mammography, tomosynthesis, ultrasound, and MRI; step-by-step approaches for image-guided breast interventions; and high-yield chapters organized by specific imaging finding seen on mammography, tomosynthesis, ultrasound, and MRI. Part of the Rotations in Radiology series, this book offers a guided approach to breast imaging interpretation and techniques, highlighting the nuances necessary to arrive at the best diagnosis and management. Each chapter contains a targeted discussion of an imaging finding which reviews the anatomy and physiology, distinguishing features, imaging techniques, differential diagnosis, clinical issues, key points, and further reading. Breast Imaging is a must-read for residents and practicing radiologists seeking a foundation for the essential knowledge base in breast imaging.

Artificial Intelligence Applications and Innovations

Artificial Intelligence Applications and Innovations
Author: Ilias Maglogiannis,Kostas Karpouzis
Publsiher: Springer
Total Pages: 744
Release: 2006-08-29
Genre: Computers
ISBN: 9780387342245

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Artificial Intelligence applications build on a rich and proven theoretical background to provide solutions to a wide range of real life problems. The ever expanding abundance of information and computing power enables researchers and users to tackle higly interesting issues for the first time, such as applications providing personalized access and interactivity to multimodal information based on preferences and semantic concepts or human-machine interface systems utilizing information on the affective state of the user. The purpose of the 3rd IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI) is to bring together researchers, engineers, and practitioners interested in the technical advances and business and industrial applications of intelligent systems. AIAI 2006 is focused on providing insights on how AI can be implemented in real world applications.

Artificial Intelligence for Data Driven Medical Diagnosis

Artificial Intelligence for Data Driven Medical Diagnosis
Author: Deepak Gupta,Utku Kose,Bao Le Nguyen,Siddhartha Bhattacharyya
Publsiher: Walter de Gruyter GmbH & Co KG
Total Pages: 326
Release: 2021-01-28
Genre: Computers
ISBN: 9783110668384

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This book collects research works of data-driven medical diagnosis done via Artificial Intelligence based solutions, such as Machine Learning, Deep Learning and Intelligent Optimization. Physical devices powered with Artificial Intelligence are gaining importance in diagnosis and healthcare. Medical data from different sources can also be analyzed via Artificial Intelligence techniques for more effective results.

Artificial Intelligence and Machine Learning in Healthcare

Artificial Intelligence and Machine Learning in Healthcare
Author: Ankur Saxena,Shivani Chandra
Publsiher: Springer Nature
Total Pages: 228
Release: 2021-05-06
Genre: Science
ISBN: 9789811608117

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This book reviews the application of artificial intelligence and machine learning in healthcare. It discusses integrating the principles of computer science, life science, and statistics incorporated into statistical models using existing data, discovering patterns in data to extract the information, and predicting the changes and diseases based on this data and models. The initial chapters of the book cover the practical applications of artificial intelligence for disease prognosis & management. Further, the role of artificial intelligence and machine learning is discussed with reference to specific diseases like diabetes mellitus, cancer, mycobacterium tuberculosis, and Covid-19. The chapters provide working examples on how different types of healthcare data can be used to develop models and predict diseases using machine learning and artificial intelligence. The book also touches upon precision medicine, personalized medicine, and transfer learning, with the real examples. Further, it also discusses the use of machine learning and artificial intelligence for visualization, prediction, detection, and diagnosis of Covid -19. This book is a valuable source of information for programmers, healthcare professionals, and researchers interested in understanding the applications of artificial intelligence and machine learning in healthcare.

Artificial Intelligence in Radiology An Issue of Radiologic Clinics of North America E Book

Artificial Intelligence in Radiology  An Issue of Radiologic Clinics of North America  E Book
Author: Daniel L. Rubin
Publsiher: Elsevier Health Sciences
Total Pages: 240
Release: 2021-10-27
Genre: Medical
ISBN: 9780323813563

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Artificial Intelligence in Radiology, An Issue of Radiologic Clinics of North America, E-Book

Artificial Intelligence in Medical Imaging

Artificial Intelligence in Medical Imaging
Author: Erik R. Ranschaert,Sergey Morozov,Paul R. Algra
Publsiher: Springer
Total Pages: 373
Release: 2019-01-29
Genre: Medical
ISBN: 9783319948782

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This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.

Artificial Intelligence in Health Care System

Artificial Intelligence in Health Care System
Author: Artificial Intelligence in Health Care System
Publsiher: Nitya Publications
Total Pages: 250
Release: 2022-01-01
Genre: Computers
ISBN: 9789391669423

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Artificial Intelligence Techniques in Breast Cancer Diagnosis and Prognosis

Artificial Intelligence Techniques in Breast Cancer Diagnosis and Prognosis
Author: Ashlesha Jain,Ajita Jain,Sandhya Jain,Lakhmi Jain
Publsiher: World Scientific
Total Pages: 348
Release: 2000-08-21
Genre: Computers
ISBN: 9789814492676

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The main aim of this book is to present a sample of recent research on the application of novel artificial intelligence paradigms to the diagnosis and prognosis of breast cancer. These paradigms include neural networks, fuzzy logic and evolutionary computing. Artificial intelligence techniques offer advantages — such as adaptation, fault tolerance, learning and human-like behavior — over conventional computing techniques. The idea is to combine the pathological, intelligent and statistical approaches to enable simple and accurate diagnosis and prognosis. This book is the first of its kind on the topic of artificial intelligence in breast cancer. It presents the applications of artificial intelligence in breast cancer diagnosis and prognosis, and includes state-of-the-art concepts in the field. It contains contributions from Australia, Germany, Italy, UK and the USA. Contents:An Introduction to Breast Cancer Diagnosis, Prognosis, and Artificial Intelligence (N Harbeck et al.)Automatic Image Feature Extraction for Diagnosis and Prognosis of Breast Cancer (M J Bottema et al.)Decision Support in Breast Cancer: Recent Advances in Prognostic and Predictive Techniques (R Kates et al.)MammoNet: A Bayesian Network Diagnosing Breast Cancer (L M Roberts)Predicting Prognosis and Treatment Response in Breast Cancer Patients (M G Daidone & D Coradini)Computer-Aided Breast Cancer Diagnosis (H-P Chan et al.)Which Decision Support Technologies are Appropriate for the Cytodiagnosis of Breast Cancer? (S S Cross et al.)Xcyt: A System for Remote Cytological Diagnosis and Prognosis of Breast Cancer (W N Street) Readership: Medical practitioners, researchers and graduate students. Keywords:Artificial Intelligence;Soft Computing;Breast Cancer;Diagnosis;Prognosis;Fuzzy Systems;Neural NetworksReviews:“The editors have done an excellent job of putting together a book that highlights the advances and controversies that surround the subject … The book will be of particular interest to clinical decision support systems designers and academic oncologists.”Cancer Forum

Advanced Machine Learning Approaches in Cancer Prognosis

Advanced Machine Learning Approaches in Cancer Prognosis
Author: Janmenjoy Nayak,Margarita N. Favorskaya,Seema Jain,Bighnaraj Naik,Manohar Mishra
Publsiher: Springer Nature
Total Pages: 454
Release: 2021-05-29
Genre: Technology & Engineering
ISBN: 9783030719753

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This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic, and hybrid intelligent systems for the determination and diagnosis of cancer. Moreover, the tactical solutions of machine learning have proved its vast range of significance and, provided novel solutions in the medical field for the diagnosis of disease. This book also explores the distinct deep learning approaches that are capable of yielding more accurate outcomes for the diagnosis of cancer. In addition to providing an overview of the emerging machine and deep learning approaches, it also enlightens an insight on how to evaluate the efficiency and appropriateness of such techniques and analysis of cancer data used in the cancer diagnosis. Therefore, this book focuses on the recent advancements in the machine learning and deep learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted audience including scientists, experts, Ph.D. students, postdocs, and anyone interested in the subjects discussed.