Model-driven Self-aware Self-training Framework for Label Noise-tolerant Medical Image Segmentation

[1]  Dingwen Zhang,et al.  Reliable Mutual Distillation for Medical Image Segmentation Under Imperfect Annotations , 2023, IEEE Transactions on Medical Imaging.

[2]  Leyuan Fang,et al.  Querying Labeled for Unlabeled: Cross-Image Semantic Consistency Guided Semi-Supervised Semantic Segmentation , 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Xiaoliu Luo,et al.  Intermediate prototype network for few-shot segmentation , 2022, Signal Process..

[4]  Guotai Wang,et al.  Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision , 2022, MICCAI.

[5]  Tieyong Zeng,et al.  Learning multi-level structural information for small organ segmentation , 2021, Signal Process..

[6]  Yilong Yin,et al.  Learning to Rectify for Robust Learning with Noisy Labels , 2021, Pattern Recognit..

[7]  Yiqiu Shen,et al.  Adaptive Early-Learning Correction for Segmentation from Noisy Annotations , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Yefeng Zheng,et al.  All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-Supervised Medical Image Segmentation , 2021, IEEE Journal of Biomedical and Health Informatics.

[9]  Jialin Shi,et al.  Distilling effective supervision for robust medical image segmentation with noisy labels , 2021, MICCAI.

[10]  Qi Tian,et al.  Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation , 2021, ECCV Workshops.

[11]  Varun Jampani,et al.  Adaptive Prototype Learning and Allocation for Few-Shot Segmentation , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Jiashi Feng,et al.  Source Data-Absent Unsupervised Domain Adaptation Through Hypothesis Transfer and Labeling Transfer , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Yuanyuan Wang,et al.  A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging , 2020, Medical Image Anal..

[14]  Pheng-Ann Heng,et al.  Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation , 2020, MICCAI.

[15]  Shuguang Cui,et al.  Characterizing Label Errors: Confident Learning for Noisy-Labeled Image Segmentation , 2020, MICCAI.

[16]  Lequan Yu,et al.  Robust Medical Image Segmentation from Non-expert Annotations with Tri-network , 2020, MICCAI.

[17]  Qixiang Ye,et al.  Prototype Mixture Models for Few-shot Semantic Segmentation , 2020, ECCV.

[18]  Gang Niu,et al.  Searching to Exploit Memorization Effect in Learning with Noisy Labels , 2020, ICML.

[19]  Martin Jägersand,et al.  AMP: Adaptive Masked Proxies for Few-Shot Segmentation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[20]  Binqiang Zhao,et al.  O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[21]  Khoi Nguyen,et al.  Feature Weighting and Boosting for Few-Shot Segmentation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[22]  Jiashi Feng,et al.  PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[23]  James Bailey,et al.  Symmetric Cross Entropy for Robust Learning With Noisy Labels , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[24]  Haidong Zhu,et al.  Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation , 2019, MICCAI.

[25]  Ghassan Hamarneh,et al.  Learning to Segment Skin Lesions from Noisy Annotations , 2019, DART/MIL3ID@MICCAI.

[26]  Hao Chen,et al.  Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[27]  Xingrui Yu,et al.  How does Disagreement Help Generalization against Label Corruption? , 2019, ICML.

[28]  Mert R. Sabuncu,et al.  Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels , 2018, NeurIPS.

[29]  Xingrui Yu,et al.  Co-teaching: Robust training of deep neural networks with extremely noisy labels , 2018, NeurIPS.

[30]  Bin Yang,et al.  Learning to Reweight Examples for Robust Deep Learning , 2018, ICML.

[31]  George Papandreou,et al.  Rethinking Atrous Convolution for Semantic Image Segmentation , 2017, ArXiv.

[32]  Richard S. Zemel,et al.  Prototypical Networks for Few-shot Learning , 2017, NIPS.

[33]  Jacob Goldberger,et al.  Training deep neural-networks based on unreliable labels , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[34]  Lawrence O. Hall,et al.  Active cleaning of label noise , 2016, Pattern Recognit..

[35]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[36]  Zhiwei Xiong,et al.  DualRel: Semi-Supervised Mitochondria Segmentation from A Prototype Perspective , 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[37]  Caiming Zhang,et al.  Anti-noise FCM image segmentation method based on quadratic polynomial , 2021, Signal Process..

[38]  Jeff A. Bilmes,et al.  Robust Curriculum Learning: from clean label detection to noisy label self-correction , 2021, ICLR.