LEADER 01286nam0 22002893i 450 001 PUV0760932 005 20231121125618.0 100 $a20171120d1873 ||||0itac50 ba 101 | $alat 102 $ade 181 1$6z01$ai $bxxxe 182 1$6z01$an 200 1 $aDissertatio Literaria continens observationes criticas in Saturas D. Iunii Iuvenalis quam annuente summo numine ...$fAndreas Scholte 210 $aTraiecti ad Rhenum$cKemink et filium$d1873 215 $a114 p.$d22 cm. 517 1 $aObservationes criticas in Saturas D. Iunii Iuvenalis$9PUV0760939 606 $aGiovenale, Decimo Giunio . Satirae$2FIR$3RMLC437076$9E 676 $a877.01$9Satira e umorismo latino. Periodo romano, fino al 499 ca.$v22 700 1$aScholte$b, Andreas$3PUVV298489$4070$01442221 801 3$aIT$bIT-01$c20171120 850 $aIT-FR0017 899 $aBiblioteca umanistica Giorgio Aprea$bFR0017 $eN 912 $aPUV0760932 950 0$aBiblioteca umanistica Giorgio Aprea$d 52S.SIJ. LL3 Iuv.Scl.$e 52DUP0009008635 VMB RS Volume in fotocopia$fA $h20171120$i20171120 977 $a 52 996 $aDissertatio Literaria continens observationes criticas in Saturas D. Iunii Iuvenalis quam annuente summo numine ..$93614340 997 $aUNICAS LEADER 07319nam 22008295 450 001 996630866503316 005 20250630101743.0 010 $a981-9784-96-4 024 7 $a10.1007/978-981-97-8496-7 035 $a(CKB)36527924300041 035 $a(MiAaPQ)EBC31783168 035 $a(Au-PeEL)EBL31783168 035 $a(OCoLC)1467875827 035 $a(DE-He213)978-981-97-8496-7 035 $a(EXLCZ)9936527924300041 100 $a20241102d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPattern Recognition and Computer Vision $e7th Chinese Conference, PRCV 2024, Urumqi, China, October 18?20, 2024, Proceedings, Part XIV /$fedited by Zhouchen Lin, Ming-Ming Cheng, Ran He, Kurban Ubul, Wushouer Silamu, Hongbin Zha, Jie Zhou, Cheng-Lin Liu 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (586 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v15044 311 08$a981-9784-95-6 327 $aA Fine-grained Recurrent Network for Image Segmentation via Vector Field Guided Refinement -- Semi-supervised Medical Image Segmentation with Strong/Weak Task-aware Consistency -- Steerable Pyramid Transform Enables Robust Left Ventricle Quantification -- Semantics Guided Disentangled GAN for Chest X-ray Image Rib Segmentation -- MedPrompt: Cross-Modal Prompting for Multi-Task Medical Image Translation -- Enhancing Hippocampus Segmentation: Swin -- UNETR Model Optimization with CPS -- Uncertainty-inspired Credible Pseudo-Labeling in Semi-Supervised Medical Image Segmentation -- MFPNet: Mixed Feature Perception Network for Automated Skin Lesion Segmentation -- LD-BSAM:Combined Latent Diffusion with Bounding SAM for HIFU target region segmentation -- Hierarchical Decoder with Parallel Transformer and CNN for Medical Image Segmentation. -CLASS-AWARE CROSS PSEUDO SUPERVISION FRAMEWORK FOR SEMI-SUPERVISED MULTI-ORGAN SEGMENTATION IN ABDOMINAL CT -- SCANSAPAN: Anti-curriculum Pseudo-labelling and Adversarial Noises Training for Semi-supervised Medical Image Classification -- Multi-Modal Learning for Predicting the Progression of Transarterial Chemoembolization Therapy in Hepatocellular Carcinoma -- Growing with the help of multiple teachers: lightweight and noise-resistant student model for medical image classification -- DRA-CN: A novel Dual-Resolution Attention Capsule Network for Histopathology Image Classification -- A Mask Guided Network for Self-Supervised Low-Dose CT ImagingDental Diagnosis from X-Ray Panoramic Radiography Images: A Dataset and A Hybrid Framework -- Edge-Guided Bidirectional-Attention Residual Network for Polyp SegmentationFrom Coarse to Fine: A Novel Colon Polyp Segmentation Method Like Human Observation -- Pseudo-Prompt Generating in Pre-trained Vision-Language Models for Multi-Label Medical Image Classification -- Multi-Perspective Text-Guided Multimodal Fusion Network for Brain Tumor Segmentation -- Continual Learning for Fundus Image Segmentation -- Embedded Deep Learning Based CT Images for Rifampicin Resistant Tuberculosis Diagnosis -- Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation -- Meply: A Large-scale Dataset and Baseline Evaluations for Metastatic Perirectal Lymph Node Segmentation -- Swin-HAUnet: A Swin-Hierarchical Attention Unet For Enhanced Medical Image Segmentation -- ODC-SA Net: Orthogonal Direction Enhancement and Scale Aware Network for Polyp Segmentation -- Two-Stage Multi-Scale Feature Fusion for Small Medical Object Segmentation -- A Two-Stage Automatic Collateral Scoring Framework Based on Brain Vessel Segmentation -- SPARK: Cross-Guided Knowledge Distillation with Spatial Position Augmentation for Medical Image Segmentation -- VATBoost-Net: Integrating Enhanced Feature Perturbation and Detail Enhancement for Medical Image Segmentation -- DTIL-Net: Dual-Task Interactive Learning Network for Automated Grading of Diabetic Retinopathy and Macular Edema -- DeformSegNet: Segmentation Network Fused with Deformation Field for Pancreatic CT Scans -- InsSegLN: A Novel 3D Instance Segmentation Method for Mediastinal Lymph NodeRRANet: A Reverse Region-Aware Network with Edge Difference for Accurate Breast Tumor Segmentation in Ultrasound ImagesLearning Frequency and Structure in UDA for Medical Object Detection -- Skin Lesion Segmentation Method Based On Global Pixel Weighted Focal Loss -- Competing Dual-Network with Pseudo-Supervision Rectification for Semi-Supervised Medical Image Segmentation -- Dual-Branch Perturbation and Conflict-Based Scribble-Supervised Meibomian Gland Segmentation. 330 $aThis 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in Urumqi, China, during October 18?20, 2024. The 579 full papers presented were carefully reviewed and selected from 1526 submissions. The papers cover various topics in the broad areas of pattern recognition and computer vision, including machine learning, pattern classification and cluster analysis, neural network and deep learning, low-level vision and image processing, object detection and recognition, 3D vision and reconstruction, action recognition, video analysis and understanding, document analysis and recognition, biometrics, medical image analysis, and various applications. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v15044 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aArtificial intelligence 606 $aApplication software 606 $aComputer networks 606 $aComputer systems 606 $aMachine learning 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aArtificial Intelligence 606 $aComputer and Information Systems Applications 606 $aComputer Communication Networks 606 $aComputer System Implementation 606 $aMachine Learning 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 0$aArtificial intelligence. 615 0$aApplication software. 615 0$aComputer networks. 615 0$aComputer systems. 615 0$aMachine learning. 615 14$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aArtificial Intelligence. 615 24$aComputer and Information Systems Applications. 615 24$aComputer Communication Networks. 615 24$aComputer System Implementation. 615 24$aMachine Learning. 676 $a006.37 700 $aLin$b Zhouchen$0908694 701 $aCheng$b Ming-Ming$01782756 701 $aHe$b Ran$0929219 701 $aUbul$b Kurban$01782757 701 $aSilamu$b Wushouer$01782758 701 $aZha$b Hongbin$01586081 701 $aZhou$b Jie$0671967 701 $aLiu$b Cheng-Lin$0861045 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996630866503316 996 $aPattern Recognition and Computer Vision$94309254 997 $aUNISA