LEADER 03145nam 2200745 a 450 001 9910962851103321 005 20200520144314.0 010 $a9786611239770 010 $a9781281239778 010 $a1281239771 010 $a9781849209076 010 $a1849209073 010 $a9781847877451 010 $a1847877451 035 $a(CKB)1000000000402902 035 $a(EBL)334378 035 $a(OCoLC)233573460 035 $a(SSID)ssj0000140761 035 $a(PQKBManifestationID)11163197 035 $a(PQKBTitleCode)TC0000140761 035 $a(PQKBWorkID)10053203 035 $a(PQKB)10771835 035 $a(MiAaPQ)EBC334378 035 $a(StDuBDS)EDZ0000018491 035 $a(Au-PeEL)EBL334378 035 $a(CaPaEBR)ebr10218247 035 $a(CaONFJC)MIL123977 035 $a(OCoLC)76836965 035 $a(FINmELB)ELB138627 035 $a(PPN)227906098 035 $a(FR-PaCSA)88869713 035 $a(FRCYB88869713)88869713 035 $a(EXLCZ)991000000000402902 100 $a20060117d2006 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aDoing your undergraduate project /$fDenis F. Reardon 205 $a1st ed. 210 $aThousand Oaks, CA $cSAGE Publications$d2006 215 $a1 online resource (xii, 244 p.) $cill 225 1 $aSAGE essential study skills 300 $aDescription based upon print version of record. 311 08$a9780761942061 311 08$a0761942068 311 08$a9780761942078 311 08$a0761942076 320 $aIncludes bibliographical references (p. [237]-238) and index. 327 $aCover; Contents; List of figures and tables; Preface; Acknowledgements; Chapter 1 - The Value of a Project; Chapter 2 - Preparing to Do Your project; Chapter 3 - Choosing a Topic; Chapter 4 - Writing the Project Proposal; Chapter 5 - Planning the Project; Chapter 6 - Risk Assessment and Management; Chapter 7 - Methodology; Chapter 8 - The Literature Review; Chapter 9 - Using Results; Chapter 10 - Writing the Project Report; Appendix 1: Sample Project Record Forms; Appendix 2: Sample work schedule, monthly breakdown; Appendix 3: Literature Item Review Record Form 327 $aAppendix 4: Literature Item Location Record Form Appendix 5: Steps to Making a Written Proposal; Appendix 6: Relationship of the Project Proposal to the Project Report; Appendix 7: Checklist for Determining Confidence in your Methodology; Appendix 8: A Method of Working on the Literature Review; Appendix 9: Databases; Bibliography; Index 330 8 $aThis work is a practical step-by-step guide to managing and developing a successful undergraduate project. 410 0$aSAGE Essential Study Skills Series 606 $aReport writing$xStudy and teaching (Higher) 615 0$aReport writing$xStudy and teaching (Higher) 676 $a378.170281 700 $aReardon$b Denis$01798491 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910962851103321 996 $aDoing your undergraduate project$94341297 997 $aUNINA LEADER 04952nam 22006615 450 001 9910987689203321 005 20251106132640.0 010 $a3-031-80965-3 024 7 $a10.1007/978-3-031-80965-1 035 $a(CKB)37877306000041 035 $a(DE-He213)978-3-031-80965-1 035 $a(MiAaPQ)EBC31958888 035 $a(Au-PeEL)EBL31958888 035 $a(EXLCZ)9937877306000041 100 $a20250313d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGenerative Machine Learning Models in Medical Image Computing /$fedited by Le Zhang, Chen Chen, Zeju Li, Greg Slabaugh 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (VIII, 382 p. 107 illus., 97 illus. in color.) 311 08$a3-031-80964-5 327 $aPart I Segmentation -- Synthesis of annotated data for medical image segmentation -- Diffusion Models For Histopathological Image Generation -- Generative AI Techniques for Ultrasound Image Reconstruction -- Part II Detection and Classification -- Vision Language Pre training from Synthetic Data -- Diffusion models for inverse problems in medical imaging -- Virtual Elastography Ultrasound via Generative Adversarial Network and its Application to Breast Cancer Diagnosis -- Generative Adversarial Networks for Brain MR Image Synthesis and Its Clinical Validation on Multiple Sclerosis -- Histopathological Synthetic Augmentation with Generative Models -- Part III Image Super resolution and Reconstruction -- Enhancing PET with Image Generation Techniques Generating Standard dose PET from Low dose PET -- EyesGAN Synthesize human face from human eyes -- Deep Generative Models for 3D Medical Image Synthesis -- Part IV Various Applications -- Cross Modal Attention Fusion based Generative Adversarial Network for text to image synthesis -- CHeart A Conditional Spatio Temporal Generative Model for Cardiac Anatomy -- Generative Models for Synthesizing Anatomical Plausible 3D Medical Images -- Diffusion Probabilistic Models for Image Formation in MRI -- Embedding 3D CT Prior into X ray Imaging Using Generative Adversarial Networks. 330 $aGenerative Machine Learning Models in Medical Image Computing" provides a comprehensive exploration of generative modeling techniques tailored to the unique demands of medical imaging. This book presents an in-depth overview of cutting-edge generative models such as GANs, VAEs, and diffusion models, examining how they enable groundbreaking applications in medical image synthesis, reconstruction, and enhancement. Covering diverse imaging modalities like MRI, CT, and ultrasound, it illustrates how these models facilitate improvements in image quality, support data augmentation for scarce datasets, and create new avenues for predictive diagnostics. Beyond technical details, the book addresses critical challenges in deploying generative models for healthcare, including ethical concerns, interpretability, and clinical validation. With a strong focus on real-world applications, it includes case studies and implementation guidelines, guiding readers in translating theory into practice. By addressing model robustness, reproducibility, and clinical utility, this book is an essential resource for researchers, clinicians, and data scientists seeking to leverage generative models to enhance biomedical imaging and deliver impactful healthcare solutions. Combining technical rigor with practical insights, it offers a roadmap for integrating advanced generative approaches in the field of medical image computing. 606 $aMachine learning 606 $aMedical informatics 606 $aImage processing 606 $aMachine Learning 606 $aHealth Informatics 606 $aImage Processing 606 $aAprenentatge automātic$2thub 606 $aInformātica mčdica$2thub 606 $aProcessament d'imatges$2thub 608 $aLlibres electrōnics$2thub 615 0$aMachine learning. 615 0$aMedical informatics. 615 0$aImage processing. 615 14$aMachine Learning. 615 24$aHealth Informatics. 615 24$aImage Processing. 615 7$aAprenentatge automātic 615 7$aInformātica mčdica 615 7$aProcessament d'imatges 676 $a006.31 702 $aZhang$b Le$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aChen$b Chen$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLi$b Zeju$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSlabaugh$b Greg$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910987689203321 996 $aGenerative Machine Learning Models in Medical Image Computing$94340409 997 $aUNINA