Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation

Jiao, Rushi; Zhang, Yichi; Liu, Ding; Cai, Rong; Zhang, Jicong

doi:10.48550/arxiv.2207.14191

Cited by 6 publications

(7 citation statements)

References 110 publications

(150 reference statements)

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“…2) Semi-supervised learning: is a machine-learning approach that combines both labeled and unlabeled data for training models. It aims to leverage the information present in the unlabeled data to improve the performance of the model [11].…”

Section: Data Annotation Methodsmentioning

confidence: 99%

Bridging the Kuwaiti Dialect Gap in Natural Language Processing

Husain,

Alostad,

Omar

2024

IEEE Access

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The available dialectal Arabic linguistic resources are very limited in their coverage of Arabic dialects, particularly the Kuwaiti dialect. This shortage in linguistic resources creates struggles for researchers in the Natural Language Processing (NLP) field and limits the development of advanced linguistic analytical and processing tools for the Kuwaiti dialect. Many other low-resource Arabic dialects are still not explored in research due to the challenges faced during the annotators' recruitment process for dataset labeling. This paper proposes a weak supervised classification system to solve the problem of recruiting human annotators called "q8SentiLabeler". In addition, we developed a large dataset consisting of over 16.6k posts serving sentiment analysis for the Kuwaiti dialect, covering several themes and timeframes to remove any bias that might affect its content. Furthermore, we evaluate our dataset using multiple traditional machine-learning classifiers and advanced deep-learning language models to test its performance. Results demonstrate the positive potential of "q8SentiLabeler" to replace human annotators with a 93% for the pairwise percent agreement and a 0.87 for Cohen's kappa coefficient. Using the ARBERT model on our dataset, we were able to achieve 89% accuracy in the system's performance.

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Section: Data Annotation Methodsmentioning

confidence: 99%

Bridging the Kuwaiti Dialect Gap in Natural Language Processing

Husain,

Alostad,

Omar

2024

IEEE Access

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“…However, these approaches are highly dependent on large-scale annotated data due to the data-driven nature of deep networks. Different from natural images, the annotation costs of medical images are much higher since it must be done by well-trained doctors (Jiao et al, 2022;Ji et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Inherent Consistent Learning for Accurate Semi-supervised Medical Image Segmentation

Zhu¹,

Yang²,

Liu³

et al. 2023

Preprint

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Semi-supervised medical image segmentation has attracted much attention in recent years because of the high cost of medical image annotations. In this paper, we propose a novel Inherent Consistent Learning (ICL) method, aims to learn robust semantic category representations through the semantic consistency guidance of labeled and unlabeled data to help segmentation. In practice, we introduce two external modules, namely Supervised Semantic Proxy Adaptor (SSPA) and Unsupervised Semantic Consistent Learner (USCL) that is based on the attention mechanism to align the semantic category representations of labeled and unlabeled data, as well as update the global semantic representations over the entire training set. The proposed ICL is a plug-and-play scheme for various network architectures, and the two modules are not involved in the testing stage. Experimental results on three public benchmarks show that the proposed method can outperform the state-of-the-art, especially when the number of annotated data is extremely limited. Code is available at: https://github.com/zhuye98/ICL.git

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“…Semi-supervised learning is a set of training methods where a smaller number of labeled images and larger number of un-labeled images are used. 8 Contrastive learning 9 is a recently developed technique to learn useful representations from unlabeled data. The fundamental goal is to learn the similarity between the same class data (positive pair) over the dissimilarity between different class data (negative pair).…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised contrastive learning for white blood cell segmentation from label-free quantitative phase imaging

Sengupta

Fanous

et al. 2023

Medical Imaging 2023: Digital and Computational Pathology

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Quantitative phase imaging (QPI) is an advanced label-free imaging technique that can quantify pathlength changes in biological samples at the nanometer scale without staining or tagging. White blood cells (WBCs) are the key components of the body’s immune system. However, automatic detection and segmentation of WBCs from QPI data can be challenging as QPI may lack the needed intrinsic specificity and contrast compared to a stained brightfield image. Morever, typical supervised learning methods require a large number of annotations for model training which involves time-consuming efforts and clinical expertise, and may not be available often. Motivated by the effectiveness of the recently developed contrastive learning-based semi-supervised segmentation methods and considering the intrinsic properties of QPI, the main contribution of this work is to implement an efficient semi-supervised segmentation method to segment WBCs from QPI data when only a small amount of ground-truth labels are available. A DeeplabV3+ and DeeplabV2 architecture were used as a backbone of the segmentation network with a modified convolution module, specifically to capture the subtle morphological details of WBCs in QPI. The preliminary results showed significant improvement in terms of quantitative metrics compared to the supervised training methods. An exploratory study was also performed to investigate the effect of the contrastive learning module on segmentation performance. To the best of our knowledge, this is the first application of the semi-supervised contrastive learning method in the WBC segmentation framework from QPI.

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Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation

Cited by 6 publications

References 110 publications

Bridging the Kuwaiti Dialect Gap in Natural Language Processing

Bridging the Kuwaiti Dialect Gap in Natural Language Processing

Inherent Consistent Learning for Accurate Semi-supervised Medical Image Segmentation

Semi-supervised contrastive learning for white blood cell segmentation from label-free quantitative phase imaging

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