Cover Image

Enhancing Dual Network Based Semi-Supervised Medical Image Segmentation with Uncertainty-Guided Pseudo-Labeling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lu, Yunyao, Wu, Yihang, Chaddad, Ahmad, Daqqaq, Tareef, Kateb, Reem
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Semi-Supervised Medical Image Segmentation via Dual Networks
by: Lu, Yunyao, et al.
Published: (2025)

Federated Vision Transformer with Adaptive Focal Loss for Medical Image Classification
by: Zhao, Xinyuan, et al.
Published: (2026)

Towards a Transparent and Interpretable AI Model for Medical Image Classifications
by: Wen, Binbin, et al.
Published: (2025)

Deep Modeling and Optimization of Medical Image Classification
by: Wu, Yihang, et al.
Published: (2025)

Domain Adaptation Techniques for Natural and Medical Image Classification
by: Chaddad, Ahmad, et al.
Published: (2025)

FAA-CLIP: Federated Adversarial Adaptation of CLIP
by: Wu, Yihang, et al.
Published: (2025)

Generalizable and Explainable Deep Learning for Medical Image Computing: An Overview
by: Chaddad, Ahmad, et al.
Published: (2025)

GMGaze: MoE-Based Context-Aware Gaze Estimation with CLIP and Multiscale Transformer
by: Zhao, Xinyuan, et al.
Published: (2026)

Federated CLIP for Resource-Efficient Heterogeneous Medical Image Classification
by: Wu, Yihang, et al.
Published: (2025)

Synergy-Guided Regional Supervision of Pseudo Labels for Semi-Supervised Medical Image Segmentation
by: Wang, Tao, et al.
Published: (2024)

FACMIC: Federated Adaptative CLIP Model for Medical Image Classification
by: Wu, Yihang, et al.
Published: (2024)

Impact of domain adaptation in deep learning for medical image classifications
by: Wu, Yihang, et al.
Published: (2026)

Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation
by: Chen, Yuanbin, et al.
Published: (2023)

Dual Cross-image Semantic Consistency with Self-aware Pseudo Labeling for Semi-supervised Medical Image Segmentation
by: Wu, Han, et al.
Published: (2025)

Deep Modeling and Interpretation for Bladder Cancer Classification
by: Chaddad, Ahmad, et al.
Published: (2026)

ProPL: Universal Semi-Supervised Ultrasound Image Segmentation via Prompt-Guided Pseudo-Labeling
by: Chen, Yaxiong, et al.
Published: (2025)

Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation
by: Lin, Jianghang, et al.
Published: (2025)

Classification based deep learning models for lung cancer and disease using medical images
by: Chaddad, Ahmad, et al.
Published: (2025)

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation
by: Keuth, Ron, et al.
Published: (2024)

Principle-Guided Supervision for Interpretable Uncertainty in Medical Image Segmentation
by: Sui, An, et al.
Published: (2026)

SAM as the Guide: Mastering Pseudo-Label Refinement in Semi-Supervised Referring Expression Segmentation
by: Yang, Danni, et al.
Published: (2024)

Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey
by: Ran, Lingyan, et al.
Published: (2024)

nnFilterMatch: A Unified Semi-Supervised Learning Framework with Uncertainty-Aware Pseudo-Label Filtering for Efficient Medical Segmentation
by: Yang, Yi
Published: (2025)

Quality-Guided Semi-Supervised Learning for Medical Image Segmentation
by: Abhishek, Kumar, et al.
Published: (2026)

PEPL: Precision-Enhanced Pseudo-Labeling for Fine-Grained Image Classification in Semi-Supervised Learning
by: Tian, Bowen, et al.
Published: (2024)

Diverse Teaching and Label Propagation for Generic Semi-Supervised Medical Image Segmentation
by: Li, Wei, et al.
Published: (2025)

SemiGDA: Generative Dual-distribution Alignment for Semi-Supervised Medical Image Segmentation
by: Huang, Kaiwen, et al.
Published: (2026)

Bidirectional Uncertainty-Aware Region Learning for Semi-Supervised Medical Image Segmentation
by: Zhou, Shiwei, et al.
Published: (2025)

SHAP-Integrated Convolutional Diagnostic Networks for Feature-Selective Medical Analysis
by: Hu, Yan, et al.
Published: (2025)

Dual-Scale Volume Priors with Wasserstein-Based Consistency for Semi-Supervised Medical Image Segmentation
by: Meng, Junying, et al.
Published: (2025)

Semi-KAN: KAN Provides an Effective Representation for Semi-Supervised Learning in Medical Image Segmentation
by: Ye, Zanting, et al.
Published: (2025)

Enhancing Weakly-Supervised Histopathology Image Segmentation with Knowledge Distillation on MIL-Based Pseudo-Labels
by: He, Yinsheng, et al.
Published: (2024)

Diff-CL: A Novel Cross Pseudo-Supervision Method for Semi-supervised Medical Image Segmentation
by: Guo, Xiuzhen, et al.
Published: (2025)

ScribbleVS: Scribble-Supervised Medical Image Segmentation via Dynamic Competitive Pseudo Label Selection
by: Wang, Tao, et al.
Published: (2024)

HACMatch Semi-Supervised Rotation Regression with Hardness-Aware Curriculum Pseudo Labeling
by: Li, Mei, et al.
Published: (2026)

SM2C: Boost the Semi-supervised Segmentation for Medical Image by using Meta Pseudo Labels and Mixed Images
by: Wang, Yifei, et al.
Published: (2024)

BARL: Bilateral Alignment in Representation and Label Spaces for Semi-Supervised Volumetric Medical Image Segmentation
by: Gao, Shujian, et al.
Published: (2025)

A Semi-Supervised Framework for Breast Ultrasound Segmentation with Training-Free Pseudo-Label Generation and Label Refinement
by: Li, Ruili, et al.
Published: (2026)

Semi-rPPG: Semi-Supervised Remote Physiological Measurement with Curriculum Pseudo-Labeling
by: Wu, Bingjie, et al.
Published: (2025)

Zero-Shot Pseudo Labels Generation Using SAM and CLIP for Semi-Supervised Semantic Segmentation
by: Saito, Nagito, et al.
Published: (2025)