Fundamentals of Machine Learning and Deep Learning in Medicine
暫譯: 醫學中的機器學習與深度學習基礎

Borhani, Reza, Borhani, Soheila, Katsaggelos, Aggelos K.

Fundamentals of Machine Learning and Deep Learning in Medicine

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商品描述

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 reader's 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 prerequisites.

商品描述(中文翻譯)

本書為醫學系學生、研究人員及專業人士提供機器學習與深度學習在醫學領域基礎概念的易懂入門。即使讀者未必具備高等數學背景，也能藉此深入了解這項顛覆性技術及其對醫學的影響。人工智慧（artificial intelligence, AI）過去曾是電腦科學與工程學系之外少有人知的深奧領域，如今已成為廣受歡迎的技術，受到各個學術領域研究學者的廣泛應用。尤其近年來，醫學與生命科學研究人員對 AI 旗下的機器學習（machine learning）與深度學習（deep learning）子領域產生了高度興趣；過去十年間，經同儕審查的醫學期刊中，相關論文數量快速成長，便是明確證據。如今，市場對此領域高品質教育資源的需求從未如此迫切，而且未來仍將持續快速成長。

本書作者皆為專家，他們採用強調直覺理解、而非抽象數學形式化的敘事方式，揭開機器學習與深度學習經常被不必要的複雜性所籠罩的面紗，在實用性與理論嚴謹性之間取得細緻平衡，協助讀者更有效地學習。本書涵蓋的主題包括：醫學資料的數學編碼、線性迴歸與分類、非線性特徵工程、深度學習、卷積神經網路與循環神經網路，以及強化學習。每章結尾均附有一系列習題，供讀者練習並檢驗自身對內容的理解。

對於希望進一步了解機器學習與深度學習的醫學系學生、專業人士及研究人員而言，本書是理想的入門讀物。只要讀者在大學階段至少修過一門入門數學課程（例如生物統計學或微積分），便具備使用本書所需的基礎，無須其他先備條件。

作者簡介

Reza Borhani, PhD is a seasoned machine learning consultant and engineer with a depth of experience developing AI solutions for companies in the healthcare and technology sectors. He has developed and taught a wide range of undergraduate-and graduate-level courses on machine learning, deep learning, reinforcement learning, and mathematical optimization at Northwestern University where he also holds an adjunct faculty position.

Soheila Borhani, MD is a physician-scientist with extensive clinical experience in various in-patient and out-patient settings. Her medical research interests lie in the areas of cancer biology, translational oncology, bioinformatics, and the application of artificial intelligence to cancer diagnostics and prognostics. She has served as editor and reviewer for a number of peer-reviewed journals in the field, and has organized short-courses on the topic of AI in medicine.

Aggelos K. Katsaggelos, PhD is the Joseph Cummings Professor in Electrical and Computer Engineering (courtesy Computer Science and Radiology), and the deputy director of the Center for Computational Imaging and Signal Analytics in Medicine and co-lead of the Augmented Intelligence in Medical Imaging Program under the Institute for Augmented Intelligence in Medicine at Feinberg School of Medicine at Northwestern University. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), SPIE, the European Association for Signal Processing (EURASIP), and the Optical Society of America (OSA).

作者簡介(中文翻譯)

Reza Borhani 博士是一位經驗豐富的機器學習顧問與工程師，具備為醫療保健與科技領域企業開發 AI 解決方案的深厚經驗。他曾在 Northwestern University 開發並教授涵蓋機器學習、深度學習、強化學習與數學最佳化等主題的多門大學部與研究所課程，目前亦於該校擔任兼任教師。

Soheila Borhani 醫學博士是一位醫師科學家，具備在各類住院與門診環境中的豐富臨床經驗。她的醫學研究興趣包括癌症生物學、轉譯腫瘤學、生物資訊學，以及人工智慧在癌症診斷與預後評估上的應用。她曾擔任多本同儕審查期刊的編輯與審稿人，並曾籌辦以醫療 AI 為主題的短期課程。

Aggelos K. Katsaggelos 博士是 Northwestern University Feinberg School of Medicine 電機與電腦工程系 Joseph Cummings 講座教授（兼任 Computer Science 與 Radiology 聘任），也是醫學計算影像與訊號分析中心的副主任，以及該校醫學增強智慧研究所旗下醫學影像增強智慧計畫的共同負責人。他是 Institute of Electrical and Electronics Engineers（IEEE）、SPIE、European Association for Signal Processing（EURASIP）及 Optical Society of America（OSA）的 Fellow。

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