LEADER 01766nam 2200373 n 450 001 996391541403316 005 20200824121853.0 035 $a(CKB)4940000000105025 035 $a(EEBO)2240919598 035 $a(UnM)99853512e 035 $a(UnM)99853512 035 $a(EXLCZ)994940000000105025 100 $a19920618d1613 uy | 101 0 $aeng 135 $aurbn||||a|bb| 200 14$aThe Messiah alreadie come. Or proofs of Christianitie$b[electronic resource] $eboth out of the Scriptures, and auncient rabbins, to convince the Iewes, of their palpable, and more then miserable blindnes (if more may be) for their long, vayne, and endles expectation of their Messiah (as they dreame) yet for to come. Written in Barbarie, in the yeare 1610, & for that cause directed to the dispersed Iewes of that countrie, & in them to all others now groaning under the yoake of this their long & intollerable captivitie: which yet one day shall have an end: .. 210 $aAmsterdam $cImprinted by Giles Thorp$dAnno M. DC. XIII [1613] 215 $a[12], 68 p 300 $aDedication to Frederick of Bohemia is signed: Iohn Harrison. 300 $aThis edition has dedication to Maurice, Prince of Orange, in English and Dutch. 300 $aIdentified as STC 12858a on UMI microfilm. 300 $aReproduction of the original in the Folger Shakespeare Library. 330 $aeebo-0055 606 $aApologetics$vEarly works to 1800 615 0$aApologetics 700 $aHarrison$b John$ffl. 1610-1638.$01011057 801 0$bCu-RivES 801 1$bCu-RivES 801 2$bCStRLIN 801 2$bWaOLN 906 $aBOOK 912 $a996391541403316 996 $aThe Messiah alreadie come. Or proofs of Christianitie$92403112 997 $aUNISA LEADER 05554nam 22006135 450 001 996647864103316 005 20250212115334.0 010 $a9783031777899 010 $a3031777891 024 7 $a10.1007/978-3-031-77789-9 035 $a(CKB)37515674100041 035 $a(MiAaPQ)EBC31903247 035 $a(Au-PeEL)EBL31903247 035 $a(OCoLC)1499721679 035 $a(DE-He213)978-3-031-77789-9 035 $a(EXLCZ)9937515674100041 100 $a20250212d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care $eFirst Deep Breast Workshop, Deep-Breath 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings /$fedited by Ritse M. Mann, Tianyu Zhang, Tao Tan, Luyi Han, Danial Truhn, Shuo Li, Yuan Gao, Shannon Doyle, Robert Martí Marly, Jakob Nikolas Kather, Katja Pinker-Domenig, Shandong Wu, Geert Litjens 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (405 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v15451 311 08$a9783031777882 311 08$a3031777883 327 $aEvaluation of Bagging Ensembles on Multimodal Data for Breast Cancer Diagnosis -- HF-Fed: Hierarchical based customized Federated Learning Framework for X-Ray Imaging -- DuEU-Net: Dual Encoder UNet with Modality-Agnostic Training for PET-CT Multi-Modal Organ and Lesion Segmentation -- One for All: UNET Training on Single-Sequence Masks for Multi-Sequence Breast MRI Segmentation -- Multimodal Breast MRI Language-Image Pretraining (MLIP): An Exploration of a Breast MRI Foundation Model -- Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data -- Efficient Generation of Synthetic Breast CT Slices By Combining Generative and Super-Resolution Models -- Exploring Patient Data Requirements in Training Effective AI Models for MRI-based Breast Cancer Classification -- Virtual dynamic contrast enhanced breast MRI using 2D U-Net -- Optimizing BI-RADS 4 Lesion Assessment using Lightweight Convolutional Neural Network with CBAM in Contrast Enhanced Mammography -- Mammographic Breast Positioning Assessment via Deep Learning -- Endpoint Detection in Breast Images for Automatic Classification of Breast Cancer Aesthetic Results -- Thick Slices for Optimal Digital Breast Tomosynthesis Classification with Deep-Learning -- Predicting Aesthetic Outcomes in Breast Cancer Surgery: a Multimodal Retrieval Approach -- Vision Mamba for Classification of Breast Ultrasound Images -- Breast Cancer Molecular Subtyping from H&E Whole Slide Images using Foundation Models and Transformers -- Graph Neural Networks for modelling breast biomechanical compression -- A generative adversarial approach to remove Moiré artifacts in Dark-field and Phase-contrast x-ray images -- MRI Breast tissue segmentation using nnUNet for Biomechanical modeling -- Fat-Suppressed Breast MRI Synthesis for Domain Adaptation in Tumour Segmentation -- Guiding Breast Conservative Surgery by Augmented Reality from Preoperative MRI: Initial System Design and Retrospective Trials -- ELK: Enhanced Learning through cross-modal Knowledge transfer for lesion detection in limited-sample contrast-enhanced mammography datasets -- Safe Breast Cancer Diagnosis Resilient to Mammographic Adversarial Samples. 330 $aThis book constitutes the refereed proceedings of the First Deep Breast Workshop on Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care, Deep-Breath 2024, held in conjunction with the 26th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, on October 10, 2024.The 23 regular papers presented in this book were carefully reviewed and selected from 51 submissions.The workshop provides an international platform for presentation of - and discussion on - studies related to AI in breast imaging. Deep-Breath aims to promote the development of this research area by sharing insights in academic research and clinical practice between clinicians and AI experts, and by exploring together the opportunities and potential challenges of AI applications in breast health. The deep-breath workshop provides, therefore, an unique forum to discuss the possibilities in this challenging field, aiming to create value that eventually truly leads to benefit for physicians and patients. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v15451 606 $aArtificial intelligence 606 $aArtificial Intelligence 615 0$aArtificial intelligence. 615 14$aArtificial Intelligence. 676 $a006.3 700 $aMann$b Ritse M$01785680 701 $aZhang$b Tianyu$01785681 701 $aTan$b Tao$01785682 701 $aHan$b Luyi$01785683 701 $aTruhn$b Danial$01785684 701 $aLi$b Shuo$01785685 701 $aGao$b Yuan$01679969 701 $aDoyle$b Shannon$01785686 701 $aMartí Marly$b Robert$01785687 701 $aKather$b Jakob Nikolas$01785688 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996647864103316 996 $aArtificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care$94317184 997 $aUNISA