Pattern Recognition and Computer Vision 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part XIII

Growth Simulation Network for Polyp Segmentation.- Brain Diffuser: An End-To-End Brain Image to Brain Network Pipeline.- CCJ-SLC: A Skin Lesion Image Classification Method based on Contrastive Clustering and Jigsaw Puzzle.- A Real-Time Network for Fast Breast Lesion Detection in Ultrasound Videos.- CBAV-Loss: Crossover and Branch Losses for Artery-vein Segmentation in OCTA Images.- Leveraging Data Correlations For Skin Lesion Classification.- CheXNet: Combing Transformer and CNN for Thorax Disease Diagnosis from Chest X-ray Images.- Cross Attention Multi Scale CNN-Transofmer Hybrid encoder is General Medical Image Learner.- Weakly/Semi-supervised Left Ventricle Segmentation in 2D Echocardiography with Uncertain Region-aware Contrastive Learning.- Spatial-Temporal Graph Convolutional Network for Insomnia Classification via Brain Functional Connectivity Imaging of rs-fMRI.- Probability-based Nuclei Detection and Critical-Region Guided Instance Segmentation.- FlashViT: A Flash Vision Transformer with Large-scale Token Merging for Congenital Heart Disease Detection.- Semi-supervised Retinal Vessel Segmentation through Point Consistency.- Knowledge Distillation of Attention and Residual U-Net: Transfer from Deep to Shallow Models for Medical Image Classification.- Two-stage deep learning segmentation for tiny brain regions.- Encoder Activation Diffusion and Decoder Transformer Fusion Network for Medical Image Segmentation.- Liver segmentation via learning cross-modality content-aware representation.- Semi-Supervised Medical Image Segmentation based on Multi-scale Knowledge Discovery and Multi-task Ensemble.- LATrans-Unet: Improving CNN-Transformer with Location-Adaptive for Medical Image Segmentation.- Adversarial Keyword Extraction and Semantic-Spatial Feature Aggregation for Clinical Report Guided Thyroid Nodule Segmentation.- A Multi-Modality Driven Promptable Transformer for Automated Parapneumonic Effusion Staging.- Assessing the Social Skills of Children with Autism Spectrum Disorder via Language-Image Pre-training Models.- PPS: Semi-supervised 3D Biomedical Image Segmentation via Pyramid Pseudo-Labeling Supervision.- A Novel Diffusion-Model-Based OCT Image Inpainting Algorithm for Wide Saturation Artifacts.- Only Classification Head is Sufficient for Medical Image Segmentation.- Task-incremental Medical Image Classification with Task-specific Batch Normalization.- Hybrid Encoded Attention Networks for Accurate Pulmonary Artery-Vein Segmentation in Noncontrast CT Images.- Multi-Modality Fusion based Lung Cancer Survival Analysis with Self-Supervised Whole Slide Image Representation Learning.- Incorporating Spiking Neural Network for Dynamic Vision Emotion Analysis.- PAT-Unet: Paired Attention Transformer for Efficient and Accurate Segmentation of 3D Medical Images.- Cell-CAEW: Cell Instance Segmentation based on ConvAttention and Enhanced Watershed.- A Comprehensive Multi-modal Domain Adaptative Aid Framework for Brain Tumor Diagnosis.- Joint Boundary-Enhanced and Topology-Preserving  Dual-Path Network for Retinal Layer Segmentation in  OCT Images with Pigment Epithelial Detachment.- Spatial Feature Regularization and Label Decoupling based Cross-Subject Motor Imagery EEG Decoding.- Autism spectrum disorder diagnosis using graph neural network based on graph pooling and self-adjust filter.- CDBIFusion: A Cross-Domain Bidirectional Interaction Fusion Network for PET and MRI Images.- LF-LVS: Label-Free Left Ventricular Segmentation for Transthoracic Echocardiogram.- Multi-atlas Representations based on Graph Convolutional Networks for Autism Spectrum Disorder Diagnosis.- MS-UNet: Swin Transformer U-Net with Multi-scale Nested Decoder for Medical Image Segmentation with Small Training Data.- GCUNET: Combining  GNN and CNN for Sinogram Restoration in Low-Dose SPECT Reconstruction.- A two-stage whole body bone SPECT scan image inpainting algorithm for residual urine artifacts based on contextual attention.