Pattern Recognition and Computer Vision: 7th Chinese Conference, PRCV 2024, Urumqi, China, October 1820, 2024, Proceedings, Part XIV 2024 ed. [Pehme köide]

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.