Fundamentals of machine learning and deep learning in medicine:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer
[2022]
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| Ausgabe: | 1st ed. 2022 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | xi, 196 Seiten Illustrationen, Diagramme |
| ISBN: | 9783031195013 |
Internformat
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Datensatz im Suchindex
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Contents 1 Introduction. The Machine Learning Pipeline. Data Collection . Feature Design. Model Training . Model Testing . A Deeper Dive into the Machine Learning Pipeline . Revisiting Data Collection . Revisiting Feature Design. Revisiting Model Training . Revisiting Model Testing . The Machine Learning Taxonomy . Problems. References. 2 Mathematical Encoding of Medical Data. Numerical Data
. Categorical Data . Imaging Data. Time-Series Data . Text Data . Genomics Data. Problems. 3 Elementary Functions and Operations . Different Representations of Mathematical Functions. Elementary Functions . Polynomial Functions . Reciprocal Functions . Trigonometric and Hyperbolic Functions . Exponential Functions . 1 3 3 4 6 7 8 8 9 11 13 14 20 22 25 25 28 30 34 37 41 43 47 47 53 54 54 55 56 ix
( .ontents X Logarithmic Functions . Step Functions. Elementary Operations . Basic Function Adjustments . Addition and Multiplication of Functions . Composition of Functions . Min-Max Operations. Constructing Complex Functions Using Elementary Functions and Operations. Problems. 58 .58 60 60 61 61 63 64 64 Linear Regression . Linear Regression with One-Dimensional Input . The Least Squares Cost Function . Linear Regression with Multi-Dimensional Input . Input Normalization . Regularization . Problems. Reference. 69
69 71 74 78 82 84 87 5 Linear Classification . Linear Classification with One-Dimensional Input . The Logistic Function . The Cross-Entropy Cost Function . The Gradient Descent Algorithm . Linear Classification with Multi-Dimensional Input. Linear Classification with Multiple Classes. Problems. References. 89 89 91 94 97 Ю1 Ю6 109 110 4 . 6 From Feature Engineering to Deep Learning . Ш Feature Engineering for Nonlinear Regression . ■ · · 111 Feature Engineering for Nonlinear Classification. 1 ^ Feature Learning. 116 Multi-Layer Neural Networks . 120 Optimization of Neural Networks . 123 Design of Neural Network Architectures. 124
Problems. 127 References. 129 7 Convolutional and Recurrent Neural Networks . The Convolution Operation . Convolutional Neural Networks . Recurrence Relations . Recurrent Neural Networks . Problems. References. 131 133 I42 151 156 160 163
Contents 8 xi Reinforcement Learning. Reinforcement Learning Applications. Path-Finding AI . Automatic Control. Game-Playing AI . Autonomous Robotic Surgery . Automated Planning of Radiation Treatment . Fundamental Concepts . States, Actions, and Rewards in Gridworld . States, Actions, and Rewards in Cart-Pole. States, Actions, and Rewards in Chess . States, Actions, and Rewards in Radiotherapy Planning . Mathematical Notation . Bellman’s Equation. The Basic Q-Learning Algorithm . The Testing Phase of Q-Learning . Tuning the Q-Learning Parameters. ß-Learning
Enhancements. The Exploration-Exploitation Trade-Off. The Short-Term Long-Term Reward Trade-Off . Tackling Problems with Large State Spaces. Problems. References. 165 165 166 167 168 168 169 170 172 172 173 173 173 175 176 178 181 182 183 184 186 187 189 Index. 191
Reza Borhani · Soheila Borhani ■ Aggelos K. Katsaggelos Fundamentals of Machine Learning and Deep Learning in Medicine This book provides an accessible introduction to the foundations of machine learning and deep learning in medicine for medical students, researchers, and professionals who are not necessarily initiated in advanced mathematics but yearn for a better understanding of this disruptive technology and its impact on medicine. Once an esoteric subject known to few outside of computer science and engineering departments, today artificial intelligence (AI) is a widely popular technology used by scholars from all across the academic universe. In particular, recent years have seen a great deal of interest in the AI subfields of machine learning and deep learning from researchers in medicine and life sciences, evidenced by the rapid growth in the number of articles published on the topic in peer-reviewed medical journals over the last decade. The demand for high-quality educational resources in this area has never been greater than it is today, and will only continue to grow at a rapid pace. Expert authors remove the veil of unnecessary complexity that often surrounds machine learning and deep learning by employing a narrative style that emphasizes intuition in place of abstract mathematical formalisms, allowing them to strike a delicate balance between practicality and theoretical rigor in service of facilitating the readers learning experience. Topics covered in the book include: mathematical encoding of medical data, linear regression and classification, nonlinear
feature engineering, deep learning, convolutional and recurrent neural networks, and reinforcement learning. Each chapter ends with a collection of exercises for readers to practice and test their knowledge. This is an ideal introduction for medical students, professionals, and researchers interested in learning more about machine learning and deep learning. Readers who have taken at least one introductory mathematics course at the undergraduate-level (e.g., biostatistics or calculus) will be well-equipped to use this book without needing any additional |
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| dewey-ones | 616 - Diseases |
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| dewey-tens | 610 - Medicine and health |
| discipline | Medizin |
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| edition | 1st ed. 2022 |
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| spelling | Borhani, Reza Verfasser (DE-588)1115510444 aut Fundamentals of machine learning and deep learning in medicine Reza Borhani, Soheila Borhani, Aggelos K. Katsaggelos Cham Springer [2022] xi, 196 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Internal Medicine Machine Learning Medizin (DE-588)4038243-6 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Medizin (DE-588)4038243-6 s DE-604 Borhani, Soheila Verfasser aut Katsaggelos, Aggelos K. 1956- Verfasser (DE-588)1205331530 aut Erscheint auch als Online-Ausgabe 978-3-031-19502-0 Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034988006&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034988006&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Borhani, Reza Borhani, Soheila Katsaggelos, Aggelos K. 1956- Fundamentals of machine learning and deep learning in medicine Internal Medicine Machine Learning Medizin (DE-588)4038243-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
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| title | Fundamentals of machine learning and deep learning in medicine |
| title_auth | Fundamentals of machine learning and deep learning in medicine |
| title_exact_search | Fundamentals of machine learning and deep learning in medicine |
| title_exact_search_txtP | Fundamentals of Machine Learning and Deep Learning in Medicine |
| title_full | Fundamentals of machine learning and deep learning in medicine Reza Borhani, Soheila Borhani, Aggelos K. Katsaggelos |
| title_fullStr | Fundamentals of machine learning and deep learning in medicine Reza Borhani, Soheila Borhani, Aggelos K. Katsaggelos |
| title_full_unstemmed | Fundamentals of machine learning and deep learning in medicine Reza Borhani, Soheila Borhani, Aggelos K. Katsaggelos |
| title_short | Fundamentals of machine learning and deep learning in medicine |
| title_sort | fundamentals of machine learning and deep learning in medicine |
| topic | Internal Medicine Machine Learning Medizin (DE-588)4038243-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Internal Medicine Machine Learning Medizin Maschinelles Lernen |
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