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  • Produktbild: Pattern Recognition and Computer Vision
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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

Fr. 126.00

inkl. gesetzl. MwSt., Versandkostenfrei

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 GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

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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.