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A Two-Stream Mutual Attention Network for Semi-supervised Biomedical Segmentation with Noisy Labels [article]

Shaobo Min, Xuejin Chen, Zheng-Jun Zha, Feng Wu, Yongdong Zhang
2018 arXiv   pre-print
In this paper, we propose a Two-Stream Mutual Attention Network (TSMAN) that weakens the influence of back-propagated gradients caused by incorrect labels, thereby rendering the network robust to unclean  ...  Learning-based methods suffer from a deficiency of clean annotations, especially in biomedical segmentation.  ...  This also demonstrates that TSMAN is robust to noisy labels. Conclusion In this paper, we propose a two-stream mutual attention network (TSMAN) that is robust to noisy labels.  ... 
arXiv:1807.11719v3 fatcat:2qvalspasrfevhtbbtue2v52ju

A Two-Stream Mutual Attention Network for Semi-Supervised Biomedical Segmentation with Noisy Labels

Shaobo Min, Xuejin Chen, Zheng-Jun Zha, Feng Wu, Yongdong Zhang
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
In this paper, we propose a Two-Stream Mutual Attention Network (TSMAN) that weakens the influence of back-propagated gradients caused by incorrect labels, thereby rendering the network robust to unclean  ...  Learning-based methods suffer from a deficiency of clean annotations, especially in biomedical segmentation.  ...  This also demonstrates that TSMAN is robust to noisy labels. Conclusion In this paper, we propose a two-stream mutual attention network (TSMAN) that is robust to noisy labels.  ... 
doi:10.1609/aaai.v33i01.33014578 fatcat:2izgzjmyivdrlfgwuiuebbtw4y

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models [article]

Jialin Peng, Ye Wang
2021 arXiv   pre-print
application of deep learning models in medical image segmentation.  ...  The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling.  ...  To weaken the influence of the noise pseudo labels in semi-supervised segmentation, Min et al.  ... 
arXiv:2103.00429v1 fatcat:p44a5e34sre4nasea5kjvva55e

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models

Jialin Peng, Ye Wang
2021 IEEE Access  
INDEX TERMS Medical image segmentation, semi-supervised segmentation, partially-supervised segmentation, noisy label, sparse annotation. 36828  ...  application of deep learning models in medical image segmentation.  ...  To weaken the influence of the noise pseudo labels in semi-supervised segmentation, Min et al.  ... 
doi:10.1109/access.2021.3062380 fatcat:r5vsec2yfzcy5nk7wusiftyayu

Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation [article]

Yichi Zhang, Qingcheng Liao, Rushi Jiao, Jicong Zhang
2021 arXiv   pre-print
Medical image segmentation is a fundamental and critical step in many clinical approaches.  ...  Semi-supervised learning has been widely applied to medical image segmentation tasks since it alleviates the heavy burden of acquiring expert-examined annotations and takes the advantage of unlabeled data  ...  Zhang, “A two-stream mutual attention network for semi-supervised biomedical segmentation V.  ... 
arXiv:2112.02508v1 fatcat:ofgv42dygvhyxphgh2wbcgdvoy

A Noise-robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions from CT Images

Guotai Wang, Xinglong Liu, Chaoping Li, Zhiyong Xu, Jiugen Ruan, Haifeng Zhu, Tao Meng, Kang Li, Ning Huang, Shaoting Zhang
2020 IEEE Transactions on Medical Imaging  
To this end, we propose a novel noise-robust framework to learn from noisy labels for the segmentation task.  ...  Learning from noisy training labels that are easier to obtain has a potential to alleviate this problem.  ...  However, both of them require a set of clean labels for training. In [34] , an attention network was proposed for semi-supervised biomedical image segmentation with noisy labels.  ... 
doi:10.1109/tmi.2020.3000314 pmid:32730215 fatcat:pvophx7p7remxoxllst2fevvma

Context-aware virtual adversarial training for anatomically-plausible segmentation [article]

Ping Wang and Jizong Peng and Marco Pedersoli and Yuanfeng Zhou and Caiming Zhang and Christian Desrosiers
2021 arXiv   pre-print
Despite their outstanding accuracy, semi-supervised segmentation methods based on deep neural networks can still yield predictions that are considered anatomically impossible by clinicians, for instance  ...  The proposed method offers a generic and efficient way to add any constraint on top of any segmentation network.  ...  A robust deep attention network to noisy labels in semi-supervised biomedical segmentation. arXiv preprint arXiv:1807.11719 .  ... 
arXiv:2107.05532v2 fatcat:ksdfzfugqjdqretsilmvrcxodu

Semi-Supervised Learning With Deep Embedded Clustering for Image Classification and Segmentation

Joseph Enguehard, Peter O'Halloran, Ali Gholipour
2019 IEEE Access  
Deep neural networks usually require large labeled datasets to construct accurate models; however, in many real-world scenarios, such as medical image segmentation, labelling data is a time-consuming and  ...  training as well as a semi-supervised method based on pseudo-labelling.  ...  The code associated with the proposed method for image segmentation can be accessed through https://github.com/josephenguehard/Semi-Supervised-Segmentation.  ... 
doi:10.1109/access.2019.2891970 pmid:31588387 pmcid:PMC6777718 fatcat:tf4q53w2njahdlvauetocnmmhq

DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data [article]

Soumick Chatterjee, Kartik Prabhu, Mahantesh Pattadkal, Gerda Bortsova, Chompunuch Sarasaen, Florian Dubost, Hendrik Mattern, Marleen de Bruijne, Oliver Speck, Andreas Nürnberger
2021 arXiv   pre-print
Deep learning model based on U-Net Multi-Scale Supervision was trained using the training subset and were made equivariant to elastic deformations in a self-supervised manner using deformation-aware learning  ...  This paper proposes a deep learning architecture to automatically segment small vessels in 7 Tesla 3D Time-of-Flight (ToF) Magnetic Resonance Angiography (MRA) data.  ...  ., 2016) and Attention U-Net (Oktay et al., 2018) have proven to be promising techniques in biomedical image segmentation (Litjens et al., 2017) .  ... 
arXiv:2006.10802v2 fatcat:dfy7tjgcvvghdegklf3y6adax4

Meta Corrupted Pixels Mining for Medical Image Segmentation [article]

Jixin Wang, Sanping Zhou, Chaowei Fang, Le Wang, Jinjun Wang
2020 arXiv   pre-print
Our method is targeted at automatically estimate a weighting map to evaluate the importance of every pixel in the learning of segmentation network.  ...  Deep neural networks have achieved satisfactory performance in piles of medical image analysis tasks.  ...  [17] proposed an attention based semi-supervised deep networks, which adopted the adversarial learning strategy to deal with the insufficient data problem in training complex networks.  ... 
arXiv:2007.03538v1 fatcat:caioptfd6vd4npmubuuah33gli

Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbation [article]

Hong-Yu Zhou, Chengdi Wang, Haofeng Li, Gang Wang, Weimin Li, Yizhou Yu
2021 medRxiv   pre-print
However, as a fundamental task, semi-supervised object detection has not gained enough attention in the field of medical image analysis.  ...  Semi-Supervised classification and segmentation methods have been widely investigated in medical image analysis.  ...  A soft-label based semi-supervised segmentation approach was presented in [4] to improve the ventricle segmentation of 2D cine MR images.  ... 
doi:10.1101/2021.06.02.21258256 fatcat:a4pgatvy7re5xkiceml2ihyq7e

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 538-550 Semi-Supervised Robust Mixture Models in RKHS for Abnormality Detection in Medical Images.  ...  ., +, TIP 2020 9204-9219 Semi-Supervised Robust Mixture Models in RKHS for Abnormality Detec- tion in Medical Images.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Toward Open-World Electroencephalogram Decoding Via Deep Learning: A Comprehensive Survey [article]

Xun Chen, Chang Li, Aiping Liu, Martin J. McKeown, Ruobing Qian, Z. Jane Wang
2021 arXiv   pre-print
In recent years, deep learning (DL) has emerged as a potential solution for such problems due to its superior capacity in feature extraction.  ...  Although various DL methods have been proposed to tackle some of the challenges in EEG decoding, a systematic tutorial overview, particularly for open-world applications, is currently lacking.  ...  She has been a research associate at the University of Maryland, College Park from 2002 to 2004.  ... 
arXiv:2112.06654v2 fatcat:roxf5k7ypfcvtdzz3pbho3kdri

Annotation-efficient deep learning for automatic medical image segmentation [article]

Shanshan Wang, Cheng Li, Rongpin Wang, Zaiyi Liu, Meiyun Wang, Hongna Tan, Yaping Wu, Xinfeng Liu, Hui Sun, Rui Yang, Xin Liu, Jie Chen (+3 others)
2021 arXiv   pre-print
The 10-fold enhanced efficiency in utilizing expert labels has the potential to promote a wide range of biomedical applications.  ...  We further test AIDE in a real-life case study for breast tumor segmentation.  ...  By contrast, much less attention has been given to noisy label learning in medical imaging 16, 50 .  ... 
arXiv:2012.04885v3 fatcat:hsmypf4ixzgyrbm4nvxf5e6rye

Upgraded W-Net with Attention Gates and its Application in Unsupervised 3D Liver Segmentation [article]

Dhanunjaya Mitta, Soumick Chatterjee, Oliver Speck, Andreas Nürnberger
2020 arXiv   pre-print
Manual or semi-automated segmentation, however, can be a time-consuming task. Most deep learning based automated segmentation methods are supervised and rely on manually segmented ground-truth.  ...  We use a W-Net architecture and modified it, such that it can be applied to 3D volumes. In addition, to suppress noise in the segmentation we added attention gates to the skip connections.  ...  ACKNOWLEDGEMENTS This work was in part conducted within the context of the International Graduate School MEMoRIAL at the Otto von Guericke University (OVGU) Magdeburg, Germany, kindly supported by the  ... 
arXiv:2011.10654v1 fatcat:pb65aaaq3vagfn2vlwmz3unmuu
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