06291nam 2200493 450 99646448600331620231110220606.03-030-88552-6(CKB)4100000012037908(MiAaPQ)EBC6737923(Au-PeEL)EBL6737923(OCoLC)1272989641(PPN)25805204X(EXLCZ)99410000001203790820220628d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMachine learning for medical image reconstruction 4th International Workshop, MLMIR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings /edited by Nandinee Haq [and four others]Cham, Switzerland :Springer,[2021]©20211 online resource (147 pages)Lecture Notes in Computer Science ;v.12964Includes index.3-030-88551-8 Intro -- Preface -- Organization -- Contents -- Deep Learning for Magnetic Resonance Imaging -- HyperRecon: Regularization-Agnostic CS-MRI Reconstruction with Hypernetworks -- 1 Introduction -- 2 Background -- 2.1 Amortized Optimization of CS-MRI -- 2.2 Hypernetworks -- 3 Proposed Method -- 3.1 Regularization-Agnostic Reconstruction Network -- 3.2 Training -- 4 Experiments -- 4.1 Hypernetwork Capacity and Hyperparameter Sampling -- 4.2 Range of Reconstructions -- 5 Conclusion -- References -- Efficient Image Registration Network for Non-Rigid Cardiac Motion Estimation -- 1 Introduction -- 2 Method -- 2.1 Network Architecture -- 2.2 Self-supervised Loss Function -- 2.3 Enhancement Mask (EM) -- 3 Experiments -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- .26em plus .1em minus .1emEvaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge*-6pt -- 1 Introduction -- 2 Methods -- 2.1 Image Perturbations -- 2.2 Description of 2019 fastMRI Approaches -- 3 Results -- 4 Discussion and Conclusion -- References -- Self-supervised Dynamic MRI Reconstruction -- 1 Introduction -- 2 Theory -- 2.1 Dynamic MRI Reconstruction -- 2.2 Self-supervised Learning -- 3 Methods -- 4 Experimental Results -- 5 Conclusion -- References -- A Simulation Pipeline to Generate Realistic Breast Images for Learning DCE-MRI Reconstruction -- 1 Introduction -- 2 Method -- 2.1 DCE-MRI Data Acquisition -- 2.2 Pharmacokinetics Model Analysis and Simulation -- 2.3 MR Acquisition Simulation -- 2.4 Testing with ML Reconstruction -- 3 Result -- 4 Discussion -- 5 Conclusion -- References -- Deep MRI Reconstruction with Generative Vision Transformers -- 1 Introduction -- 2 Theory -- 2.1 Deep Unsupervised MRI Reconstruction -- 2.2 Generative Vision Transformers -- 3 Methods.4 Results -- 5 Discussion -- 6 Conclusion -- References -- Distortion Removal and Deblurring of Single-Shot DWI MRI Scans -- 1 Introduction -- 2 Background -- 2.1 Distortion Removal Framework -- 2.2 EDSR Architecture -- 3 Distortion Removal and Deblurring of EPI-DWI -- 3.1 Data -- 3.2 Distortion Removal Using Structural Images -- 3.3 Pre-processing for Super-Resolution -- 3.4 Data Augmentation -- 3.5 Architectures Explored for EPI-DWI Deblurring -- 4 Experiments and Results -- 4.1 Computer Hardware Details -- 4.2 Training Details -- 4.3 Baselines -- 4.4 Evaluation Metrics -- 4.5 Results -- 5 Conclusion -- References -- One Network to Solve Them All: A Sequential Multi-task Joint Learning Network Framework for MR Imaging Pipeline -- 1 Introduction -- 2 Method -- 2.1 SampNet: The Sampling Pattern Learning Network -- 2.2 ReconNet: The Reconstruction Network -- 2.3 SegNet: The Segmentation Network -- 2.4 SemuNet: The Sequential Multi-task Joint Learning Network Framework -- 3 Experiments and Discussion -- 3.1 Experimental Details -- 3.2 Experiments Results -- 4 Limitation, Discussion and Conclusion -- References -- Physics-Informed Self-supervised Deep Learning Reconstruction for Accelerated First-Pass Perfusion Cardiac MRI -- 1 Introduction -- 2 Methods -- 2.1 Conventional FPP-CMR Reconstruction -- 2.2 Supervised Learning Reconstruction: MoDL -- 2.3 SECRET Reconstruction -- 2.4 Dataset -- 2.5 Implementation Details -- 3 Results and Discussion -- 4 Conclusion -- References -- Deep Learning for General Image Reconstruction -- Noise2Stack: Improving Image Restoration by Learning from Volumetric Data -- 1 Introduction and Related Work -- 2 Methods -- 3 Experiments -- 3.1 MRI -- 3.2 Microscopy -- 4 Discussion -- 5 Conclusion -- References -- Real-Time Video Denoising to Reduce Ionizing Radiation Exposure in Fluoroscopic Imaging -- 1 Introduction.1.1 Background -- 1.2 Our Contributions -- 2 Methods -- 2.1 Data -- 2.2 Training Pair Simulation -- 2.3 Denoising Model -- 2.4 Model Training -- 3 Experiments -- 3.1 Reader Study -- 3.2 Video Quality -- 3.3 Runtime -- 4 Conclusion -- References -- A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolution -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Frequency Domain Analysis -- 3.2 Frequency Domain Loss -- 4 Experiments -- 4.1 Dataset -- 4.2 Training Details -- 4.3 Results -- 4.4 Ablation Study -- 5 Conclusion -- References -- Semi- and Self-supervised Multi-view Fusion of 3D Microscopy Images Using Generative Adversarial Networks -- 1 Introduction -- 2 Related Work -- 3 Methods -- 4 Experiments and Results -- 4.1 Datasets -- 4.2 Existing Methods for Comparison -- 4.3 CNN-Based Multi-View Deconvolution and Fusion -- 5 Conclusions -- References -- Author Index.Lecture Notes in Computer Science Diagnostic imagingData processingCongressesArtificial intelligenceMedical applicationsCongressesDiagnostic imagingData processingArtificial intelligenceMedical applications006.31Haq NandineeMiAaPQMiAaPQMiAaPQBOOK996464486003316Machine Learning for Medical Image Reconstruction1912511UNISA05584oam 2200781I 450 991078671340332120230803025941.01-136-27341-70-203-11006-41-136-27342-510.4324/9780203110065 (CKB)2670000000355026(EBL)1181114(OCoLC)842909464(SSID)ssj0000885218(PQKBManifestationID)12429604(PQKBTitleCode)TC0000885218(PQKBWorkID)10953417(PQKB)11381344(MiAaPQ)EBC1181114(MiAaPQ)EBC4838551(Au-PeEL)EBL1181114(CaPaEBR)ebr10691807(CaONFJC)MIL485232(Au-PeEL)EBL4838551(CaPaEBR)ebr11372256(OCoLC)983736395(FINmELB)ELB133828(EXLCZ)99267000000035502620180706d2013 uy 0engur|n|---|||||txtccrDeveloping a forensic practice operations and ethics for experts /William H. ReidNew York :Routledge,2013.1 online resource (321 p.)Includes index.0-415-53776-2 0-415-81705-6 Cover; Title; Copyright; Dedication; CONTENTS; Preface; 1 Getting Started; 2 Vocabulary; 3 Lawyer-Expert Relationships; 4 Records and Record Review; 5 Evaluations; 6 Reports and Affidavits; 7 Deposition and Trial Testimony; 8 Fees and Billing; 9 Ethics; 10 Marketing; 11 Your Office and Office Procedures; 12 Liability in Forensic Practice; 13 A Lawyer's Perspective on Forensic Mental Health Experts; Appendices: Forms, Letters, Reports, and More; Internal Documents, Letters, Communications; Report Examples; A. Initial Attorney Letter; B. Fee Sheet; C. Settlement AcknowledgmentD. Evaluation Appointment LetterE. Evaluee Information Sheet; F. Notification of Treatment Need Discovered During Evaluation; G. Subpoena Duces Tecum Response; H. Pre-Testimony Deposit Worksheet; I. Pre-Testimony Deposit Letter; J. Time Worksheet; K. Vendor Confidentiality Agreement; L. Employee Confidentiality Agreement; R1. Report: Trial Competency (Fitness to Proceed) (Simple); R2. Report: Trial Competency (Fitness to Proceed) (Complex); R3. Report: Criminal Responsibility (Sanity); R4. Report: Criminal Defense, Mitigation of Charge or Sentence; R5. Report: NGRI Release, DefenseR6. Report: Personal Injury Defense (PTSD)R7. Report: Clinician-Patient Sex, Plaintiff; R8. Report: Malpractice, Plaintiff (Complex, Doctor and Hospital); R9. Affidavit: Malpractice, Plaintiff Pre-Suit; R10. Letter/Report: Malpractice, Plaintiff Pre-Suit, Lack of Causation; R11. Report: Malpractice, Plaintiff (Complex); R12. Report: Malpractice, Defense (Complex, Facility); R13. Report: Malpractice, Defense (Facility), Forensic Practice Standards; R14. Report: Malpractice, Defense (Clinician) (Alleged Fetal Damage from Medication); R15. Report: Accidental Overdose vs. SuicideR16. Report: Defense, Death in CustodyR17. Affidavit: Defense Rebuttal, Death in Custody; R18. Report: Workplace Stressors Allegedly Causing Suicide, Expert Report Rebuttal; R19. Report: Private Insurance Disability Appeal (Complex); R20. Report: Employee Emotional Injury, Treater-Expert Conflict; R21. Report: Professional Licensing Agency Review; R22. Report: Professional Licensing Agency Review; R23. Opinion Letter: Professional Licensure; R24. Report: Civil Capacity, Contracting; R25. Report: Capacity, Guardianship (Complex, Contested)R26. Opinion Letter: Capacity, Business, and TestamentaryR27. Report: Auto Accident vs. Suicide; R28. Affidavit: Supporting Motion to Strike Expert Testimony (Forensic Practice Standards); R29. Letter: Rebuttal of Expert's Report, Forensic Practice Standards; Index"Although there are a lot of positive reasons for expanding a clinical practice into forensic work, it requires special knowledge and experience. Dr. William Reid, who maintains one of the largest and most-visited forensic psychiatry/psychology websites on the Internet and is one of the most experienced and reputable forensic mental health professionals in North America, has written a short yet practical guide for mental health professionals who wish to change or expand their practices into forensic work. It contains everything practitioners need to know about beginning and maintaining a successful and ethical forensic practice. It is be useful, quickly readable, and easy to understand with lots of summary texts and examples"--Provided by publisher.Forensic PsychiatryethicsForensic Psychiatryorganization & administrationExpert TestimonyethicsExpert Testimonylegislation & jurisprudenceLiability, LegalPractice ManagementForensic Psychiatryethics.Forensic Psychiatryorganization & administration.Expert Testimonyethics.Expert Testimonylegislation & jurisprudence.Liability, Legal.Practice Management.614/.15068Reid William H.1945-,1501481MiAaPQMiAaPQMiAaPQBOOK9910786713403321Developing a forensic practice3728607UNINA