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Pattern Recognition and Computer Vision 7th Chinese Conference, PRCV 2024, Urumqi, China, October 18–20, 2024, Proceedings, Part XIV

Aus der Reihe Lecture Notes in Computer Science
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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

03.11.2024

Herausgeber

Zhouchen Lin + weitere

Verlag

Springer Singapore

Seitenzahl

573

Maße (L/B/H)

23,5/15,5/3,2 cm

Gewicht

885 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-981-9784-95-0

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

03.11.2024

Herausgeber

Verlag

Springer Singapore

Seitenzahl

573

Maße (L/B/H)

23,5/15,5/3,2 cm

Gewicht

885 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-981-9784-95-0

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Pattern Recognition and Computer Vision
  • Produktbild: Pattern Recognition and Computer Vision

  • 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 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.