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[null,"1.","Record Nr.",null,"UNISALENTO991002850629707536"]
[null,null,"Autore",null,"Muschg, Adolf"]
[null,null,"Titolo",null,"Die japanische Tasche: Roman / Adolf Muschg"]
[null,null,"Pubbl/distr/stampa",null,"München : C.H. Beck, [2015]"]
[null,null,"ISBN",null,"9783406682018"]
[null,null,"Descrizione fisica",null,"484 p. ; 23 cm"]
[null,null,"Disciplina",null,"830"]
[null,null,"Lingua di pubblicazione",null,"Tedesco"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNISA996630866503316"]
[null,null,"Autore",null,"Lin Zhouchen"]
[null,null,"Titolo",null,"Pattern Recognition and Computer Vision : 7th Chinese Conference, PRCV 2024, Urumqi, China, October 18–20, 2024, Proceedings, Part XIV / / edited by Zhouchen Lin, Ming-Ming Cheng, Ran He, Kurban Ubul, Wushouer Silamu, Hongbin Zha, Jie Zhou, Cheng-Lin Liu"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"981-9784-96-4"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (586 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 15044"]
[null,null,"Disciplina",null,"006.37"]
[null,null,"Soggetti",null,"Image processing - Digital techniques"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Computer systems"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Computer Imaging, Vision, Pattern Recognition and Graphics"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[{"$nulls":4},"Computer Communication Networks"]
[{"$nulls":4},"Computer System Implementation"]
[{"$nulls":4},"Machine Learning"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"A 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 "]
[{"$nulls":4},"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."]
[null,null,null,"Sommario/riassunto","This 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, "]
[{"$nulls":4},"and various applications."]