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A Two-Stream Mutual Attention Network for Semi-supervised Biomedical Segmentation with Noisy Labels
[article]
2018
arXiv
pre-print
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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
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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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]
2021
arXiv
pre-print
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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
2021
IEEE Access
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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]
2021
arXiv
pre-print
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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
2020
IEEE Transactions on Medical Imaging
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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]
2021
arXiv
pre-print
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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
2019
IEEE Access
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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]
2021
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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]
2021
medRxiv
pre-print
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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
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., +, 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]
2021
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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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