LEADER 04109nam 22006735 450 001 9910731459803321 005 20240509122938.0 010 $a981-9918-39-1 024 7 $a10.1007/978-981-99-1839-3 035 $a(MiAaPQ)EBC30601987 035 $a(Au-PeEL)EBL30601987 035 $a(DE-He213)978-981-99-1839-3 035 $a(PPN)27226136X 035 $a(CKB)27060364500041 035 $a(EXLCZ)9927060364500041 100 $a20230615d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learning and Medical Applications /$fedited by Jin Keun Seo 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (349 pages) 225 1 $aMathematics in Industry,$x2198-3283 ;$v40 311 08$aPrint version: Seo, Jin Keun Deep Learning and Medical Applications Singapore : Springer,c2023 9789819918386 320 $aIncludes bibliographical references. 327 $aIntroduction -- Image Processing Techniques -- Medical image computing using Computeruzed Tomography -- Multiphysics imaging modalities using MRI (electrical, mechanical, optical) -- Imaging modalities using electrodes -- Multiphysics imaging modalities using ultrasound and light -- Emerging tissue property imaging. 330 $aOver the past 40 years, diagnostic medical imaging has undergone remarkable advancements in CT, MRI, and ultrasound technology. Today, the field is experiencing a major paradigm shift, thanks to significant and rapid progress in deep learning techniques. As a result, numerous innovative AI-based programs have been developed to improve image quality and enhance clinical workflows, leading to more efficient and accurate diagnoses. AI advancements of medical imaging not only address existing unsolved problems but also present new and complex challenges. Solutions to these challenges can improve image quality and reveal new information currently obscured by noise, artifacts, or other signals. Holistic insight is the key to solving these challenges. Such insight may lead to a creative solution only when it is based on a thorough understanding of existing methods and unmet demands. This book focuses on advanced topics in medical imaging modalities, including CT and ultrasound, with the aim of providing practical applications in the healthcare industry. It strikes a balance between mathematical theory, numerical practice, and clinical applications, offering comprehensive coverage from basic to advanced levels of mathematical theories, deep learning techniques, and algorithm implementation details. Moreover, it provides in-depth insights into the latest advancements in dental cone-beam CT, fetal ultrasound, and bioimpedance, making it an essential resource for professionals seeking to stay up-to-date with the latest developments in the field of medical imaging. 410 0$aMathematics in Industry,$x2198-3283 ;$v40 606 $aMathematical models 606 $aMathematical analysis 606 $aMathematics 606 $aMathematical Modeling and Industrial Mathematics 606 $aAnalysis 606 $aApplications of Mathematics 606 $aIntel·ligència artificial en medicina$2thub 606 $aAprenentatge automàtic$2thub 606 $aEnginyeria biomèdica$2thub 608 $aLlibres electrònics$2thub 615 0$aMathematical models. 615 0$aMathematical analysis. 615 0$aMathematics. 615 14$aMathematical Modeling and Industrial Mathematics. 615 24$aAnalysis. 615 24$aApplications of Mathematics. 615 7$aIntel·ligència artificial en medicina 615 7$aAprenentatge automàtic. 615 7$aEnginyeria biomèdica 676 $a610.28563 702 $aSeo$b Jin Keun 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910731459803321 996 $aDeep learning and medical applications$93554141 997 $aUNINA