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Where is the disease? Semi-supervised pseudo-normality synthesis from an abnormal image
[article]
2021
arXiv
pre-print
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We propose a Semi-supervised Medical Image generative LEarning network (SMILE) which not only utilizes limited medical images with segmentation masks, but also leverages massive medical images without ...
Moreover, the proposed semi-supervised learning achieves comparable medical image synthesis quality with supervised learning model, using only 50 of segmentation data. ...
A confidence enhancement technique is introduced for semi-supervised generative learning. ...
arXiv:2106.15345v1
fatcat:7elvvtrf6ngsjlsz2mfvaf2bum
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation
[article]
2023
arXiv
pre-print
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2022 pre-print arXiv:2207.14191v2 fatcat:j3o3vg5vd5dsxg5i3y6ovxlreyOther Versions
In this paper, we present a comprehensive review of recently proposed semi-supervised learning methods for medical image segmentation and summarized both the technical novelties and empirical results. ...
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. ...
ANALYSIS OF EMPIRICAL RESULTS FOR SEMI-SUPERVISED MEDICAL IMAGE SEGMENTATION
Common Evaluation Metrics for Medical Image Segmentation For medical image segmentation tasks, Dice Similarity Coefficient ...
arXiv:2207.14191v3
fatcat:gva2fzpos5efxfbod5kb4axm5a
Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images
[article]
2021
arXiv
pre-print
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To tackle the challenge of limited annotations, in this paper, we propose an uncertainty-guided dual-consistency learning network (UDC-Net) for semi-supervised COVID-19 lesion segmentation from CT images ...
We then quantify the segmentation uncertainty in two forms and employ them together to guide the consistency regularization for more reliable unsupervised learning. ...
Dual-consistency Learning for Semi-supervised Segmentation Image-level Consistency Learning via transformation equivalence of deep segmentation models f seg indicates that while a transformation T (·) ...
arXiv:2104.03225v2
fatcat:shaahzvnafgo5ivx5wptcvcste
Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation
[article]
2021
arXiv
pre-print
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To address this issue, we propose a semi-supervised image segmentation technique based on the concept of multi-view learning. ...
Segmentation of images is a long-standing challenge in medical AI. ...
We propose a dual-view learning scheme for semi-supervised medical image segmentation. 2. ...
arXiv:2108.11154v1
fatcat:u2qmon2xbjaapadczm4r6e5h3e
Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation
[article]
2023
arXiv
pre-print
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To address these issues, we propose an Uncertainty-guided Collaborative Mean-Teacher (UCMT) for semi-supervised semantic segmentation with the high-confidence pseudo labels. ...
High-quality pseudo labels are essential for semi-supervised semantic segmentation. ...
Conclusion We present an uncertainty-guided collaborative mean-teacher for semi-supervised medical image segmentation. ...
arXiv:2301.04465v1
fatcat:xbqmoslqh5bffj4x7o54hmlgwy
Enhancing Point Annotations with Superpixel and Confidence Learning Guided for Improving Semi-Supervised OCT Fluid Segmentation
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2306.02582v2 fatcat:bpuddkaiyvfuhpczinkkumvcy4Other Versions
To address this, we propose Superpixel and Confident Learning Guide Point Annotations Network (SCLGPA-Net) based on the teacher-student architecture, which can learn OCT fluid segmentation from limited ...
Although semi-supervised OCT fluid segmentation networks enhance their performance by introducing additional unlabeled data, the performance enhancement is limited. ...
Semi-Supervised Segmentation Considerable efforts have been devoted to advancing semi-supervised medical image segmentation . ...
arXiv:2306.02582v3
fatcat:35p47wrmurggrgm7h2whv4jnnu
Uncertainty guided semi-supervised segmentation of retinal layers in OCT images
[article]
2021
arXiv
pre-print
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The proposed semi-supervised segmentation framework is a key contribution and applicable for biomedical image segmentation across various imaging modalities where access to annotated medical images is ...
In this paper, we propose a novel uncertainty-guided semi-supervised learning based on a student-teacher approach for training the segmentation network using limited labeled samples and a large number ...
Proposed Semi-supervised Segmentation Method In this section, we describe our proposed uncertainty guided semi-supervised learning. ...
arXiv:2103.02083v1
fatcat:qhq2lb2i55g5rfbdrkbxh3vjnm
Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation
[article]
2022
arXiv
pre-print
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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 ...
Medical image segmentation is a fundamental and critical step in many clinical approaches. ...
In this paper, we present a novel uncertainty-guided mutual consistency learning framework for semi-supervised medical image segmentation. ...
arXiv:2112.02508v2
fatcat:dnppas5hhvgl3ejmrvg245wmj4
Teach me to segment with mixed supervision: Confident students become masters
[article]
2020
arXiv
pre-print
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branch, and guides the entropy (student-confidence) term to avoid trivial solutions. ...
Furthermore, we discuss an interesting link between Shannon-entropy minimization and standard pseudo-mask generation and argue that the former should be preferred over the latter for leveraging information ...
Motivated by these observations, we propose a novel formulation for learning with mixed supervision in medical image segmentation. ...
arXiv:2012.08051v1
fatcat:kyduresnynakdkronhjzpkv3mu
Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentations
[article]
2023
arXiv
pre-print
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unsupervised masks in a semi-supervised multi-task setting. ...
However, generating corresponding segmentation masks for synthetic medical images is laborious and subjective. ...
We also propose an innovative multi-task learning based semi-supervised strategy for the synthesis of segmentation masks. ...
arXiv:2303.12747v1
fatcat:ewwdxhkl6zfozet5wrmaqt2iby
Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2302.03296v1 fatcat:7mkvlp2gcjbhzcujtmgwv77dgu
Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-guided interventions, and ...
Among these, studies on transfer learning leveraging external datasets, semi-supervised learning using unannotated datasets and partially-supervised learning integrating partially-labeled datasets have ...
[32] investigated non-fully supervised medical image segmentation methods including weakly supervised, transfer learning, active learning, and semi-supervised learning paradigms, etc. Ma et al. ...
arXiv:2302.03296v2
fatcat:6bchgeat7beobpu72sfpgvfaua
Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation
2024
Expert systems with applications
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2024 pre-print arXiv:2403.12767v1 fatcat:u3zngupc2jaixly65led62jb34
We also propose a new two-stage network with pseudo-mask guided feature aggregation (PG-FANet) as the segmentation model. ...
Various semi-supervised learning approaches have been developed to work with limited ground truth annotations, such as the popular teacher-student models. ...
Pseudo-mask guided feature aggregation network (PG-FANet) For effectively improving learning ability, we propose a feature aggregation network for both supervised and semi-supervised learning processes ...
doi:10.1016/j.eswa.2023.122093
fatcat:lcrmmydqcvbmjia5rbaefd5sfe
PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training
[article]
2022
arXiv
pre-print
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semi-supervised methods, which demonstrates its effectiveness for medical image segmentation. ...
In this paper, we propose a new semi-supervised adversarial method called Patch Confidence Adversarial Training (PCA) for medical image segmentation. ...
CONCLUSION In this paper, we propose a novel semi-supervised learning model (PCA) for medical image segmentation. ...
arXiv:2207.11683v1
fatcat:ll2drex7dbefdayuz6wwyq2ycq
Self and Mixed Supervision to Improve Training Labels for Multi-Class Medical Image Segmentation
[article]
2024
arXiv
pre-print
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Accurate training labels are a key component for multi-class medical image segmentation. Their annotation is costly and time-consuming because it requires domain expertise. ...
This work aims to develop a dual-branch network and automatically improve training labels for multi-class image segmentation. ...
INTRODUCTION Medical image segmentation is a fundamental task in medical image analysis by delineating a medical image into multiple meaningful regions 1 . ...
arXiv:2403.03882v1
fatcat:joumanfdbzhzritazu5agzga6i
Enhancing Pseudo Label Quality for Semi-Supervised Domain-Generalized Medical Image Segmentation
[article]
2022
arXiv
pre-print
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This paper presents a novel confidence-aware cross pseudo supervision algorithm for semi-supervised domain generalized medical image segmentation. ...
Generalizing the medical image segmentation algorithms to unseen domains is an important research topic for computer-aided diagnosis and surgery. ...
Acknowledgments This work was supported by a research grant from Shenzhen Municipal Central Government Guides Local Science and Technology Development Special Funded Projects (2021Szvup139) and a research ...
arXiv:2201.08657v2
fatcat:cehwtd6imrad3es4a27huu6hny
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