Unleashing the Potential of Open-set Noisy Samples Against Label Noise for Medical Image Classification
Fuente:
arXiv
Guardado en:
| Autores principales: | Liao, Zehui, Hu, Shishuai, Zhang, Yanning, Xia, Yong |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Instance-dependent Label Distribution Estimation for Learning with Label Noise
por: Liao, Zehui, et al.
Publicado: (2022)
por: Liao, Zehui, et al.
Publicado: (2022)
Towards Clinician-Preferred Segmentation: Leveraging Human-in-the-Loop for Test Time Adaptation in Medical Image Segmentation
por: Hu, Shishuai, et al.
Publicado: (2024)
por: Hu, Shishuai, et al.
Publicado: (2024)
Cycle Context Verification for In-Context Medical Image Segmentation
por: Hu, Shishuai, et al.
Publicado: (2025)
por: Hu, Shishuai, et al.
Publicado: (2025)
UniVRSE: Unified Vision-conditioned Response Semantic Entropy for Hallucination Detection in Medical Vision-Language Models
por: Liao, Zehui, et al.
Publicado: (2025)
por: Liao, Zehui, et al.
Publicado: (2025)
V-Loop: Visual Logical Loop Verification for Hallucination Detection in Medical Visual Question Answering
por: Jin, Mengyuan, et al.
Publicado: (2026)
por: Jin, Mengyuan, et al.
Publicado: (2026)
From Few to More: Scribble-based Medical Image Segmentation via Masked Context Modeling and Continuous Pseudo Labels
por: Wang, Zhisong, et al.
Publicado: (2024)
por: Wang, Zhisong, et al.
Publicado: (2024)
Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook
por: Ma, Yuan, et al.
Publicado: (2025)
por: Ma, Yuan, et al.
Publicado: (2025)
Deep Self-Cleansing for Medical Image Segmentation with Noisy Labels
por: Dong, Jiahua, et al.
Publicado: (2024)
por: Dong, Jiahua, et al.
Publicado: (2024)
Adaptive Label Correction for Robust Medical Image Segmentation with Noisy Labels
por: Qian, Chengxuan, et al.
Publicado: (2025)
por: Qian, Chengxuan, et al.
Publicado: (2025)
Noisy Label Classification using Label Noise Selection with Test-Time Augmentation Cross-Entropy and NoiseMix Learning
por: Lee, Hansang, et al.
Publicado: (2022)
por: Lee, Hansang, et al.
Publicado: (2022)
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels
por: Li, Jichang, et al.
Publicado: (2023)
por: Li, Jichang, et al.
Publicado: (2023)
Day-Night Adaptation: An Innovative Source-free Adaptation Framework for Medical Image Segmentation
por: Chen, Ziyang, et al.
Publicado: (2024)
por: Chen, Ziyang, et al.
Publicado: (2024)
Unleashing the Potential of Synthetic Images: A Study on Histopathology Image Classification
por: Benito-Del-Valle, Leire, et al.
Publicado: (2024)
por: Benito-Del-Valle, Leire, et al.
Publicado: (2024)
VGS-ATD: Robust Distributed Learning for Multi-Label Medical Image Classification Under Heterogeneous and Imbalanced Conditions
por: Zhao, Zehui, et al.
Publicado: (2025)
por: Zhao, Zehui, et al.
Publicado: (2025)
From Noisy Labels to Intrinsic Structure: A Geometric-Structural Dual-Guided Framework for Noise-Robust Medical Image Segmentation
por: Wang, Tao, et al.
Publicado: (2025)
por: Wang, Tao, et al.
Publicado: (2025)
Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding
por: Yin, Xingyilang, et al.
Publicado: (2025)
por: Yin, Xingyilang, et al.
Publicado: (2025)
Unleashing the Potential of the Semantic Latent Space in Diffusion Models for Image Dehazing
por: Yang, Zizheng, et al.
Publicado: (2025)
por: Yang, Zizheng, et al.
Publicado: (2025)
Imbalanced Medical Image Segmentation with Pixel-dependent Noisy Labels
por: Guo, Erjian, et al.
Publicado: (2025)
por: Guo, Erjian, et al.
Publicado: (2025)
MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation
por: Ye, Yiwen, et al.
Publicado: (2025)
por: Ye, Yiwen, et al.
Publicado: (2025)
Label Filling via Mixed Supervision for Medical Image Segmentation from Noisy Annotations
por: Li, Ming, et al.
Publicado: (2024)
por: Li, Ming, et al.
Publicado: (2024)
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise
por: Khanal, Bidur, et al.
Publicado: (2024)
por: Khanal, Bidur, et al.
Publicado: (2024)
Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding
por: Cheng, Zhiheng, et al.
Publicado: (2024)
por: Cheng, Zhiheng, et al.
Publicado: (2024)
Robust Tiny Object Detection in Aerial Images amidst Label Noise
por: Zhu, Haoran, et al.
Publicado: (2024)
por: Zhu, Haoran, et al.
Publicado: (2024)
Pre-training Everywhere: Parameter-Efficient Fine-Tuning for Medical Image Analysis via Target Parameter Pre-training
por: Lei, Xingliang, et al.
Publicado: (2024)
por: Lei, Xingliang, et al.
Publicado: (2024)
Unleashing the Potential of SAM2 for Biomedical Images and Videos: A Survey
por: Zhang, Yichi, et al.
Publicado: (2024)
por: Zhang, Yichi, et al.
Publicado: (2024)
Risk-Aware Robust Learning: Reducing Clinical Risk under Label Noise in Medical Image Classification
por: Pereira, Maycon R. S., et al.
Publicado: (2026)
por: Pereira, Maycon R. S., et al.
Publicado: (2026)
DAG: Unleash the Potential of Diffusion Model for Open-Vocabulary 3D Affordance Grounding
por: Wang, Hanqing, et al.
Publicado: (2025)
por: Wang, Hanqing, et al.
Publicado: (2025)
Debiased Noise Editing on Foundation Models for Fair Medical Image Classification
por: Jin, Ruinan, et al.
Publicado: (2024)
por: Jin, Ruinan, et al.
Publicado: (2024)
Curriculum Group Policy Optimization: Adaptive Sampling for Unleashing the Potential of Text-to-Image Generation
por: Li, Baoteng, et al.
Publicado: (2026)
por: Li, Baoteng, et al.
Publicado: (2026)
Neighboring Slice Noise2Noise: Self-Supervised Medical Image Denoising from Single Noisy Image Volume
por: Zhou, Langrui, et al.
Publicado: (2024)
por: Zhou, Langrui, et al.
Publicado: (2024)
Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise
por: Yu, Yeonguk, et al.
Publicado: (2024)
por: Yu, Yeonguk, et al.
Publicado: (2024)
Noisy Label Refinement with Semantically Reliable Synthetic Images
por: Li, Yingxuan, et al.
Publicado: (2025)
por: Li, Yingxuan, et al.
Publicado: (2025)
Noisy Label Processing for Classification: A Survey
por: Li, Mengting, et al.
Publicado: (2024)
por: Li, Mengting, et al.
Publicado: (2024)
PASS: Peer-Agreement based Sample Selection for training with Noisy Labels
por: Garg, Arpit, et al.
Publicado: (2023)
por: Garg, Arpit, et al.
Publicado: (2023)
LNL+K: Enhancing Learning with Noisy Labels Through Noise Source Knowledge Integration
por: Wang, Siqi, et al.
Publicado: (2023)
por: Wang, Siqi, et al.
Publicado: (2023)
Investigating the Robustness of Vision Transformers against Label Noise in Medical Image Classification
por: Khanal, Bidur, et al.
Publicado: (2024)
por: Khanal, Bidur, et al.
Publicado: (2024)
DiN: Diffusion Model for Robust Medical VQA with Semantic Noisy Labels
por: Guo, Erjian, et al.
Publicado: (2025)
por: Guo, Erjian, et al.
Publicado: (2025)
Enhanced Sample Selection with Confidence Tracking: Identifying Correctly Labeled yet Hard-to-Learn Samples in Noisy Data
por: Pan, Weiran, et al.
Publicado: (2025)
por: Pan, Weiran, et al.
Publicado: (2025)
Graph Attention Transformer Network for Multi-Label Image Classification
por: Yuan, Jin, et al.
Publicado: (2022)
por: Yuan, Jin, et al.
Publicado: (2022)
Dynamic Loss Decay based Robust Oriented Object Detection on Remote Sensing Images with Noisy Labels
por: Liu, Guozhang, et al.
Publicado: (2024)
por: Liu, Guozhang, et al.
Publicado: (2024)
Ejemplares similares
-
Instance-dependent Label Distribution Estimation for Learning with Label Noise
por: Liao, Zehui, et al.
Publicado: (2022) -
Towards Clinician-Preferred Segmentation: Leveraging Human-in-the-Loop for Test Time Adaptation in Medical Image Segmentation
por: Hu, Shishuai, et al.
Publicado: (2024) -
Cycle Context Verification for In-Context Medical Image Segmentation
por: Hu, Shishuai, et al.
Publicado: (2025) -
UniVRSE: Unified Vision-conditioned Response Semantic Entropy for Hallucination Detection in Medical Vision-Language Models
por: Liao, Zehui, et al.
Publicado: (2025) -
V-Loop: Visual Logical Loop Verification for Hallucination Detection in Medical Visual Question Answering
por: Jin, Mengyuan, et al.
Publicado: (2026)