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Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation
[article]
2020
arXiv
pre-print
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in our self-ensembling model. ...
With the aim of semi-supervised segmentation tasks, we introduce a transformation consistent strategy in our self-ensembling model to enhance the regularization effect for pixel-level predictions. ...
Transformation Consistent Self-ensembling Model Next, we introduce how we design the randomized data transformation regularization for segmentation, i.e., the transformation-consistent self-ensembling ...
arXiv:1903.00348v3
fatcat:tazv622govdfvmjan7ido5k5ce
Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model
[article]
2018
arXiv
pre-print
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in our self-ensembling model. ...
Aiming for the semi-supervised segmentation problem, we enhance the effect of regularization for pixel-level predictions by introducing a transformation, including rotation and flipping, consistent scheme ...
Acknowledgments We thank anonymous reviewers for the comments and suggestions. The work is supported by the Research Grants Council of the Hong Kong Special Administrative Region (Project no. ...
arXiv:1808.03887v1
fatcat:bebpxpq6xzbyda5lv36cpio2ja
Enhancing Pseudo Label Quality for Semi-Supervised Domain-Generalized Medical Image Segmentation
[article]
2022
arXiv
pre-print
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Generalizing the medical image segmentation algorithms to unseen domains is an important research topic for computer-aided diagnosis and surgery. ...
This paper presents a novel confidence-aware cross pseudo supervision algorithm for semi-supervised domain generalized medical image segmentation. ...
Existing methods for semisupervised segmentation can be broadly classified into two categories: self-training (Lee et al. 2013 ) and consistency learning (Tarvainen and Valpola 2017; Li et al. 2018b; ...
arXiv:2201.08657v2
fatcat:cehwtd6imrad3es4a27huu6hny
Semi-supervised Medical Image Segmentation via Geometry-aware Consistency Training
[article]
2022
arXiv
pre-print
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In this paper, a novel geometry-aware semi-supervised learning framework is proposed for medical image segmentation, which is a consistency-based method. ...
The performance of supervised deep learning methods for medical image segmentation is often limited by the scarcity of labeled data. ...
Semi-supervised Medical Image Segmentation To alleviate the heavy burden of manual delineation, semisupervised medical image segmentation has been widely studied for a long period. ...
arXiv:2202.06104v1
fatcat:s7yuqfhtebh3jniua5tpkawway
Enhancing Pseudo Label Quality for Semi-supervised Domain-Generalized Medical Image Segmentation
2022
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
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Generalizing the medical image segmentation algorithms to unseen domains is an important research topic for computer-aided diagnosis and surgery. ...
This paper presents a novel confidence-aware cross pseudo supervision algorithm for semi-supervised domain generalized medical image segmentation. ...
Existing methods for semisupervised segmentation can be broadly classified into two categories: self-training (Lee et al. 2013 ) and consistency learning (Tarvainen and Valpola 2017; Li et al. 2018b; ...
doi:10.1609/aaai.v36i3.20217
fatcat:bp3v64rwdjh4dl6h3afie3rno4
LCC-Net: A Lightweight Cross-Consistency Network for Semisupervised Cardiac MR Image Segmentation
2021
Computational and Mathematical Methods in Medicine
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This study addresses cardiac image segmentation in scenarios where few labeled data are available with a lightweight cross-consistency network named LCC-Net. ...
Semantic segmentation plays a crucial role in cardiac magnetic resonance (MR) image analysis. ...
Besides, mean-teacher [9] is another notable paradigm for semisupervised learning, which could be used in medical image segmentation. ...
doi:10.1155/2021/9960199
pmid:34055042
pmcid:PMC8143880
fatcat:wzyzpz6bxrffrizpp2svqg5fha
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation
[article]
2022
arXiv
pre-print
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Semi-supervised learning has emerged as an appealing strategy and been widely applied to medical image segmentation tasks to train deep models with limited annotations. ...
Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches. ...
for medical image segmentation [108] . ...
arXiv:2207.14191v1
fatcat:k47z5cbqbvhp7lzzhhfxtpt2wa
Semi-supervised Medical Image Segmentation through Dual-task Consistency
[article]
2021
arXiv
pre-print
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Meanwhile, our framework outperforms the state-of-the-art semi-supervised medical image segmentation methods. Code is available at: https://github.com/Luoxd1996/DTC ...
Simultaneously, we introduce a dual-task consistency regularization between the level set-derived segmentation maps and directly predicted segmentation maps for both labeled and unlabeled data. ...
Yechong Huang for constructive discussions, suggestion and manuscript proofread and also thank the organization teams of MICCAI 2018 left atrial segmentation challenge, the National Institutes of Health ...
arXiv:2009.04448v2
fatcat:x7bsdzugcvfdjbyrb4uqxyobr4
EVALUATION OF SEMI-SUPERVISED LEARNING FOR CNN-BASED CHANGE DETECTION
2021
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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The approach is based on the Mean Teacher method, a semi-supervised approach, successfully applied for image classification and for sematic segmentation of medical images. ...
In this work we experiment with the implementation of a semi-supervised training approach in an attempt to improve the image semantic segmentation performance of models trained using a small number of ...
applied these principles for image classification and medical image semantic segmentation. ...
doi:10.5194/isprs-archives-xliii-b3-2021-829-2021
fatcat:de3tnxo6sndlzayzc2zi5kctme
Deep Semi-supervised Segmentation with Weight-Averaged Consistency Targets
[chapter]
2018
Lecture Notes in Computer Science
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Recently proposed techniques for semi-supervised learning such as Temporal Ensembling and Mean Teacher have achieved state-of-the-art results in many important classification benchmarks. ...
from the Magnetic Resonance Imaging (MRI) domain. ...
In [20] , they use a Generative Adversarial Networks (GAN) for the semisupervised segmentation of natural images, however, they employ unrealistic dataset sizes when compared to the medical imaging domain ...
doi:10.1007/978-3-030-00889-5_2
fatcat:ac4k54oftvdldnwpkpsn7wqgje
Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model
[article]
2020
arXiv
pre-print
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It is a consistency-based method which exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations, and leverages a self-ensembling model to produce high-quality ...
However, in medical image analysis, obtaining high-quality labels for the data is laborious and expensive, as accurately annotating medical images demands expertise knowledge of the clinicians. ...
[31] extended the Π model [3] for semi-supervised skin lesion segmentation with a transformation consistency strategy. ...
arXiv:2005.07377v1
fatcat:surpqy7ekvbbbc4ygwacwueyky
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. ...
For semi-supervised medical image segmentation, Li et al. ...
arXiv:2103.00429v1
fatcat:p44a5e34sre4nasea5kjvva55e
Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation
[article]
2021
arXiv
pre-print
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for self-ensembling and cross-task consistency learning from task-level regularization to exploit geometric shape information. ...
Medical image segmentation is a fundamental and critical step in many clinical approaches. ...
Heng,
“Transformation-consistent self-ensembling model for semisupervised
medical image segmentation,” IEEE Transactions on Neural Networks
and Learning Systems, 2020.
[35] X. Luo, W. ...
arXiv:2112.02508v1
fatcat:ofgv42dygvhyxphgh2wbcgdvoy
Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation
[article]
2021
arXiv
pre-print
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In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty. ...
In particular, our CoraNet model consists of three major components: a conservative-radical module (CRM), a certain region segmentation network (C-SN), and an uncertain region segmentation network (UC-SN ...
For example, Π-model [29] adopts a self-ensembling strategy by imposing a consistency prediction constraint between two augmentations of the same sample. ...
arXiv:2110.08762v1
fatcat:adu3h2pqgba4fh2efryh6tofqi
MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels
[chapter]
2021
Lecture Notes in Computer Science
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Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2022 pre-print arXiv:2203.12454v1 fatcat:njmclusbdvgxxdmbtyeav54ngi
We aim to investigate how to efficiently leverage unlabeled data from the source and target domains with limited source annotations for cross-modality image segmentation. ...
However, annotating medical images is laborious, expensive, and requires human expertise, which induces the label scarcity problem. ...
This research is supported by Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore. ...
doi:10.1007/978-3-030-87193-2_28
fatcat:cmqrhlvgpvba5kape4qawz5eti
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