LEADER 03821nam 22006015 450 001 9911047701803321 005 20251001130750.0 010 $a3-031-94791-6 024 7 $a10.1007/978-3-031-94791-9 035 $a(MiAaPQ)EBC32326180 035 $a(Au-PeEL)EBL32326180 035 $a(CKB)41532790200041 035 $a(DE-He213)978-3-031-94791-9 035 $a(OCoLC)1547929787 035 $a(EXLCZ)9941532790200041 100 $a20251001d2026 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRecent Advances in Deep Learning for Medical Image Analysis $eParadigms and Applications /$fby Yen-Wei Chen, Lanfen Lin, Rahul Kumar Jain 205 $a1st ed. 2026. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2026. 215 $a1 online resource (375 pages) 225 1 $aIntelligent Systems Reference Library,$x1868-4408 ;$v278 311 08$a3-031-94790-8 327 $aDeep Convolutional Neural Networks (CNNs) -- Deep CNNs for Image Classification, Object Detection, and Segmentation -- Attention and Transformer Networks -- Transformer-based Approaches for Medical Image Analysis -- Deep Learning Networks for 3D Medical Image Analysis -- Multimodal Deep Learning for Medical Image Analysis -- Semi-supervised Learning for Medical Image Analysis -- Domain Adaptation and Generalization for Medical Image Analysis -- Deep Learning Models for Medical Image Translation -- Foundation Models for Medical Image Analysis. 330 $aThis book is a valuable resource for understanding the transformative role of artificial intelligence in modern healthcare and aims to inspire continued research and collaboration across disciplines. In recent years, deep learning has emerged as a transformative technology across various fields, with medical image analysis standing out as one of its most impactful applications. This book offers a comprehensive overview of the latest developments in this fast-evolving domain, bridging foundational principles with state-of-the-art techniques that are redefining the future of medical imaging. This book is structured in two parts?Part I: Deep Learning Fundamentals and Paradigms and Part II: Advanced Deep Learning for Medical Image Analysis. The book provides in-depth coverage of essential topics, including convolutional neural networks, attention mechanisms, transformer architectures, multimodal analysis, semi-supervised learning, domain adaptation, generative models, and foundation models for large-scale pretraining. This book is intended for a broad audience, including graduate students, academic researchers, and industry professionals in computer science, biomedical engineering, and healthcare technologies. It serves as both an introductory guide and a reference resource for those seeking to deepen their knowledge in this rapidly evolving area. 410 0$aIntelligent Systems Reference Library,$x1868-4408 ;$v278 606 $aEngineering$xData processing 606 $aComputational intelligence 606 $aBig data 606 $aData Engineering 606 $aComputational Intelligence 606 $aBig Data 615 0$aEngineering$xData processing. 615 0$aComputational intelligence. 615 0$aBig data. 615 14$aData Engineering. 615 24$aComputational Intelligence. 615 24$aBig Data. 676 $a620.00285 700 $aChen$b Yen-Wei$01362820 701 $aLin$b Lanfen$01861451 701 $aJain$b Rahul Kumar$01861452 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911047701803321 996 $aRecent Advances in Deep Learning for Medical Image Analysis$94467559 997 $aUNINA