Fundamentals of Machine Learning and Deep Learning in Medicine PDF
Preview Fundamentals of Machine Learning and Deep Learning in Medicine
Fundamentals of Machine Learning and Deep Learning in Medicine Reza Borhani Soheila Borhani Aggelos K. Katsaggelos Fundamentals of Machine Learning and Deep Learning in Medicine Reza Borhani • Soheila Borhani (cid:129) Aggelos K. Katsaggelos Fundamentals of Machine Learning and Deep Learning in Medicine RezaBorhani SoheilaBorhani ElectricalandComputerEngineering BiomedicalInformatics NorthwesternUniversity UniversityofTexasHealthScienceCenter Evanston,IL,USA Houston,TX,USA AggelosK.Katsaggelos ElectricalandComputerEngineering NorthwesternUniversity Evanston,IL,USA ISBN978-3-031-19501-3 ISBN978-3-031-19502-0 (eBook) https://doi.org/10.1007/978-3-031-19502-0 ©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNatureSwitzerland AG2022 Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuse ofillustrations,recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,and transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar ordissimilarmethodologynowknownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthors,andtheeditorsaresafetoassumethattheadviceandinformationinthisbook arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor theeditorsgiveawarranty,expressedorimplied,withrespecttothematerialcontainedhereinorforany errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional claimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Toourfamilies: MaryamandAli Eιρηνη,Zωη,(cid:3)oϕιαandAdam Preface Not long ago, machine learning and deep learning were esoteric subjects known onlytoaselectfewatcomputerscienceandstatisticsdepartments.Today,however, thesetechnologieshavemadetheirwayintoeverycorneroftheacademicuniverse, including medicine. From automatic segmentation of medical imaging data, to diagnosing medical conditions and disorders, to predicting clinical outcomes, to recruiting patients for clinical trials, machine learning and deep learning models have produced results that rival, and in some cases exceed, human performance. These groundbreaking successes have garnered the attention of healthcare stake- holders in academia and industry, with many anticipating and advocating for an overhaul of current educational curricula in order to prepare students for the transitionofmedicinefromthe“informationage”tothe“ageofAI.” As medical and health-related programs begin to incorporate machine learning and deep learning into their curricula, a salient question arises about the extent to which these subjects should be taught, given that researchers and practitioners in thesefieldscan,andoftendo,usevariousformsoftechnologywithoutfullknowl- edgeoftheirinner-workings.Forinstance,adiagnosticianneednotnecessarilybe familiarwithhowmagneticfieldsaregeneratedinsideascannermachineinorder to interpret an MRI accurately. Similarly, surgeons can learn to operate robotic surgical systems effectively without ever knowing how to build, fix, or maintain one. We believe the same cannot be said about the use of artificial intelligence in medicine. For example, oncologists cannot be the mere end-users of a machine learningmodelwhichrecommendsthebestcourseoftreatmentforagivencancer patient. They need to understand how these models work and, ideally, play an activeroleindevelopingthem.Otherwise,oneoftwoscenariosisboundtooccur: either physicians will uncritically accept the model recommendations (which is a dangerous form of automation bias), or they will learn to distrust and ignore such recommendations to the detriment of their patients who could benefit from the “wisdom”ofdata-drivenmodelstrainedonmillionsuponmillionsofexamples. Thanks to the immense popularity of machine learning and deep learning, the marketaboundswithtextbookswrittenonthesesubjects.However,havinggenerally been written by mathematicians and engineers for mathematicians and engineers, vii viii Preface thesetextsarenotgearedtowardthespecificeducationalneedsofmedicalstudents, researchers,andpractitioners.Putdifferently,theyarewrittenina“language”which isnotaccessibletotheaveragescholarinmedicinewhotypicallylacksagraduate- levelbackgroundinmathematicsandcomputerscience.Nearlysixdecadesago,the pioneering British medical scientist Sir Harold Percival Himsworth addressed this verychallengeinhisopeningstatementtothe1964ConferenceonMathematicsand ComputerScienceinBiologyandMedicine:“Medicalbiologists,mathematicians, physicistsandcomputologistsmayhavemoreoftheiroutlookincommonthanwe suspect. But they do speak different dialects and they do have different points of view.Thisisnonewproblemforamultidisciplinarysubjectlikemedicalresearch. Ifitistobesolvedandtheevidentnecessityforco-operationrealized,onethingis essential:wemustlearneachother’slanguage.” The book before you is an attempt to realize this vision by providing an accessibleintroductiontothefundamentalsofmachinelearninganddeeplearning inmedicine.Toserveanaudienceofmedicalresearchersandprofessionals,wehave presentedthroughoutthebookacuratedselectionofmachinelearningapplications from medicine and adjacent fields. Additionally, we have prioritized intuitive descriptions over abstract mathematical formalisms in order to remove the veil of unnecessary complexity that often surrounds machine learning and deep learning concepts. A reader who has 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. This makes our introductory text appropriate for use by readers from a wide array of medical backgrounds who are not necessarily initiated in advanced mathematics but yearn forabetterunderstandingofhowthesedisruptivetechnologiescanshapethefuture ofmedicine. Evanston,IL,USA RezaBorhani Houston,TX,USA SoheilaBorhani Evanston,IL,USA AggelosK.Katsaggelos Contents 1 Introduction .................................................................. 1 TheMachineLearningPipeline .............................................. 3 DataCollection ......................................................... 3 FeatureDesign .......................................................... 4 ModelTraining ......................................................... 6 ModelTesting .......................................................... 7 ADeeperDiveintotheMachineLearningPipeline ........................ 8 RevisitingDataCollection ............................................. 8 RevisitingFeatureDesign .............................................. 9 RevisitingModelTraining ............................................. 11 RevisitingModelTesting .............................................. 13 TheMachineLearningTaxonomy ........................................... 14 Problems....................................................................... 20 References..................................................................... 22 2 MathematicalEncodingofMedicalData ................................. 25 NumericalData ............................................................... 25 CategoricalData .............................................................. 28 ImagingData .................................................................. 30 Time-SeriesData ............................................................. 34 TextData ...................................................................... 37 GenomicsData ................................................................ 41 Problems....................................................................... 43 3 ElementaryFunctionsandOperations ................................... 47 DifferentRepresentationsofMathematicalFunctions ...................... 47 ElementaryFunctions ........................................................ 53 PolynomialFunctions .................................................. 54 ReciprocalFunctions ................................................... 54 TrigonometricandHyperbolicFunctions ............................. 55 ExponentialFunctions ................................................. 56 ix x Contents LogarithmicFunctions ................................................. 58 StepFunctions........................................................... 58 ElementaryOperations ....................................................... 60 BasicFunctionAdjustments ........................................... 60 AdditionandMultiplicationofFunctions ............................. 61 CompositionofFunctions ............................................. 61 Min–MaxOperations ................................................... 63 ConstructingComplexFunctionsUsingElementaryFunctions andOperations .......................................................... 64 Problems....................................................................... 64 4 LinearRegression ........................................................... 69 LinearRegressionwithOne-DimensionalInput ............................ 69 TheLeastSquaresCostFunction ............................................ 71 LinearRegressionwithMulti-DimensionalInput .......................... 74 InputNormalization .......................................................... 78 Regularization ................................................................ 82 Problems....................................................................... 84 Reference...................................................................... 87 5 LinearClassification ........................................................ 89 LinearClassificationwithOne-DimensionalInput ......................... 89 TheLogisticFunction ........................................................ 91 TheCross-EntropyCostFunction ........................................... 94 TheGradientDescentAlgorithm ............................................ 97 LinearClassificationwithMulti-DimensionalInput ........................ 101 LinearClassificationwithMultipleClasses ................................. 106 Problems....................................................................... 109 References..................................................................... 110 6 FromFeatureEngineeringtoDeepLearning ............................ 111 FeatureEngineeringforNonlinearRegression ............................. 111 FeatureEngineeringforNonlinearClassification ........................... 115 FeatureLearning .............................................................. 116 Multi-LayerNeuralNetworks ............................................... 120 OptimizationofNeuralNetworks ........................................... 123 DesignofNeuralNetworkArchitectures .................................... 124 Problems....................................................................... 127 References..................................................................... 129 7 ConvolutionalandRecurrentNeuralNetworks ......................... 131 TheConvolutionOperation .................................................. 133 ConvolutionalNeuralNetworks ............................................. 142 RecurrenceRelations ......................................................... 151 RecurrentNeuralNetworks .................................................. 156 Problems....................................................................... 160 References..................................................................... 163 Contents xi 8 ReinforcementLearning .................................................... 165 ReinforcementLearningApplications ....................................... 165 Path-FindingAI ........................................................ 166 AutomaticControl ...................................................... 167 Game-PlayingAI ....................................................... 168 AutonomousRoboticSurgery ......................................... 168 AutomatedPlanningofRadiationTreatment ......................... 169 FundamentalConcepts ....................................................... 170 States,Actions,andRewardsinGridworld ........................... 172 States,Actions,andRewardsinCart–Pole ............................ 172 States,Actions,andRewardsinChess ................................ 173 States,Actions,andRewardsinRadiotherapyPlanning ............. 173 MathematicalNotation ....................................................... 173 Bellman’sEquation ........................................................... 175 TheBasicQ-LearningAlgorithm ........................................... 176 TheTestingPhaseofQ-Learning ..................................... 178 TuningtheQ-LearningParameters .................................... 181 Q-LearningEnhancements ................................................... 182 TheExploration–ExploitationTrade-Off .............................. 183 TheShort-TermLong-TermRewardTrade-Off ...................... 184 TacklingProblemswithLargeStateSpaces ................................. 186 Problems....................................................................... 187 References..................................................................... 189 Index............................................................................... 191
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