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Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation [article]

Xiaomeng Li, Lequan Yu, Hao Chen, Chi-Wing Fu, Lei Xing, Pheng-Ann Heng
2020 arXiv   pre-print
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]

Xiaomeng Li, Lequan Yu, Hao Chen, Chi-Wing Fu, Pheng-Ann Heng
2018 arXiv   pre-print
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]

Huifeng Yao, Xiaowei Hu, Xiaomeng Li
2022 arXiv   pre-print
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]

Zihang Liu, Chunhui Zhao
2022 arXiv   pre-print
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

Huifeng Yao, Xiaowei Hu, Xiaomeng Li
2022 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
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

Lai Song, Jiajin Yi, Jialin Peng, Yuankai Huo
2021 Computational and Mathematical Methods in Medicine  
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]

Rushi Jiao, Yichi Zhang, Le Ding, Rong Cai, Jicong Zhang
2022 arXiv   pre-print
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]

Xiangde Luo, Jieneng Chen, Tao Song, Guotai Wang
2021 arXiv   pre-print
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

E. Bousias Alexakis, C. Armenakis
2021 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
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]

Christian S. Perone, Julien Cohen-Adad
2018 Lecture Notes in Computer Science  
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]

Quande Liu, Lequan Yu, Luyang Luo, Qi Dou, Pheng Ann Heng
2020 arXiv   pre-print
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]

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.  ...  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]

Yichi Zhang, Qingcheng Liao, Rushi Jiao, Jicong Zhang
2021 arXiv   pre-print
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]

Yinghuan Shi, Jian Zhang, Tong Ling, Jiwen Lu, Yefeng Zheng, Qian Yu, Lei Qi, Yang Gao
2021 arXiv   pre-print
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]

Ziyuan Zhao, Kaixin Xu, Shumeng Li, Zeng Zeng, Cuntai Guan
2021 Lecture Notes in Computer Science  
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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