LEADER 01765nam 2200445 n 450 001 996394377803316 005 20200824121641.0 035 $a(CKB)4940000000120417 035 $a(EEBO)2240866215 035 $a(UnM)99829014e 035 $a(UnM)99829014 035 $a(EXLCZ)994940000000120417 100 $a19950517d1679 uy | 101 0 $aeng 135 $aurbn||||a|bb| 200 10$aEnquiries to be propounded to the most ingenious of each county in my travels through England and Wales$b[electronic resource] $ein order to their history of nature and arts 210 $a[Oxford $cs.n.$d1679?] 215 $a[4] p 300 $aSigned at end: R. P. = Robert Plot. 300 $aCaption title. 300 $aDate and place of publication from Madan. 300 $aSignatures: A² . 300 $aAn outline of the questionnaire Robert Plot designed for his proposed surveys of the counties of England and Wales; only the Staffordshire volume was published (1686). Cf. Madan. 300 $aReproduction of original in the University of Illinois (Urbana-Champaign Campus). Library. 330 $aeebo-0167 606 $aLocal geography$xSurveys$vEarly works to 1800 606 $aGeography$xSurveys$zEngland$vEarly works to 1800 606 $aGeography$xSurveys$zWales$vEarly works to 1800 615 0$aLocal geography$xSurveys 615 0$aGeography$xSurveys 615 0$aGeography$xSurveys 700 $aPlot$b Robert$f1640-1696.$01005841 801 0$bCu-RivES 801 1$bCu-RivES 801 2$bCStRLIN 801 2$bWaOLN 906 $aBOOK 912 $a996394377803316 996 $aEnquiries to be propounded to the most ingenious of each county in my travels through England and Wales$92317371 997 $aUNISA LEADER 01382nam 2200457 450 001 9910792710503321 005 20230809223824.0 010 $a1-78040-713-0 035 $a(CKB)3710000001178521 035 $a(MiAaPQ)EBC4841508 035 $a(Au-PeEL)EBL4841508 035 $a(CaPaEBR)ebr11374068 035 $a(OCoLC)983738948 035 $a(EXLCZ)993710000001178521 100 $a20170503h20172017 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 00$aEnvironmental hazards $emethodologies for risk assessment and management /$fedited by Nicolas R. Dalezios 210 1$aLondon, England :$cIWA Publishing,$d2017. 210 4$d©2017 215 $a1 online resource (535 pages) 311 $a1-78040-712-2 320 $aIncludes bibliographical references at the end of each chapters and index. 606 $aEnvironmental disasters 606 $aEnvironmental risk assessment 606 $aHazard mitigation 615 0$aEnvironmental disasters. 615 0$aEnvironmental risk assessment. 615 0$aHazard mitigation. 676 $a363.7 702 $aDalezios$b Nicolas R. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910792710503321 996 $aEnvironmental hazards$92150552 997 $aUNINA LEADER 04892nam 22006375 450 001 996668465003316 005 20250716130244.0 010 $a3-031-98688-1 024 7 $a10.1007/978-3-031-98688-8 035 $a(MiAaPQ)EBC32218112 035 $a(Au-PeEL)EBL32218112 035 $a(CKB)39660059400041 035 $a(DE-He213)978-3-031-98688-8 035 $a(EXLCZ)9939660059400041 100 $a20250716d2026 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMedical Image Understanding and Analysis $e29th Annual Conference, MIUA 2025, Leeds, UK, July 15?17, 2025, Proceedings, Part I /$fedited by Sharib Ali, David C. Hogg, Michelle Peckham 205 $a1st ed. 2026. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2026. 215 $a1 online resource (475 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v15916 311 08$a3-031-98687-3 327 $a -- Frontiers in Computational Pathology. -- Transductive Survival Ranking for Pan-cancer Automatic Risk Stratification using Whole Slide Images. -- Benchmarking Histopathology Foundation Models in a Multi-center Dataset for Skin Cancer Subtyping. -- MitoNet: Efficient Ki-67 Detection in H\&E-Stained Images. -- ASTER: Automated Segmentation of Endometrial Histology Images for Reproductive Health Assessment. -- Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H\&E Images. -- SMatt-DINO: Spatially Aware Masked Attention Network for High Resolution Brain Image Classification. -- Persistent Homology and Gabor Features Reveal Inconsistencies Between Widely Used Colorectal Cancer Training and Testing Datasets. -- SWIFT-Reg: Slide-Wide Intelligent Feature-based Tissue Registration. -- Learnable Moran?s Index for Modeling Spatial Autocorrelation in Whole Slide Images to Predict Breast Cancer Outcomes. -- Image Synthesis and Generative Artificial Intelligence. -- Augmenting Chest X-ray Datasets with Non-Expert Annotations. -- Leveraging Synthetic Data for Whole-Body Segmentation in X-ray Images. -- Transform(AI)ng Radiology with CheXSBT: Integrating Dual-Attention Swin Transformer with BERT for Seamless Chest X-Ray Report Generation. -- Cardiac Ultrasound Video Generation Using a Diffusion Model with Temporal Transformer. -- KCLVA: Knowledge-enhanced Contrastive Learning and View-specific Attention for Chest X-ray Report Generation. -- BlastDiffusion: A Latent Diffusion Model for Generating Synthetic Embryo Images to Address Data Scarcity in In Vitro Fertilization. -- MediAug: Exploring Visual Augmentation in Medical Imaging. -- On the Robustness of Medical Vision-Language Models: Are they Truly Generalizable?. -- DiNO-Diffusion: Scaling Medical Diffusion Models via Self-Supervised Pre-Training. -- Knowledge-Driven Hypothesis Generation for Burn Diagnosis from Ultrasound with Vision-Language Model. -- Multimodal Federated Learning With Missing Modalities through Feature Imputation Network. -- Parameter-Efficient Multimodal Adaptation for Certified Robustness of Medical Vision-Language Models. 330 $aThe three-volume set LNCS 15916,15917 & 15918 constitutes the refereed proceedings of the 29th Annual Conference on Medical Image Understanding and Analysis, MIUA 2025, held in Leeds, UK, during July 15?17, 2025. The 67 revised full papers presented in these proceedings were carefully reviewed and selected from 99 submissions. The papers are organized in the following topical sections: Part I: Frontiers in Computational Pathology; and Image Synthesis and Generative Artificial Intelligence. Part II: Image-guided Diagnosis; and Image-guided Intervention. 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