Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation

Chaitanya, Krishna; Erdil, Ertunç; Karani, Neerav; Konukoğlu, Ender

doi:10.1016/j.media.2023.102792

Cited by 95 publications

(89 citation statements)

References 34 publications

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“…In computer vision, many works [12]- [15], [17]- [20] have proposed view-based methods for solving advanced problems, such as self-supervised, semi-supervised learning, and medical imaging. Some studies [12], [13] introduced a contrastive learning framework for self-supervised representation learning by utilizing the similarities of different views of the same instance.…”

Section: B View-based Methodsmentioning

confidence: 99%

Section: B View-based Methodsmentioning

confidence: 99%

“…Inspired by advances in computer vision, medical image analysis studies [17]- [20] proposed medical domain-specific methods that adjust to their medical domain attributes. [17], [18] attempted to integrate contrastive learning and volumetric medical domain-specific knowledge for volumetric medical segmentation. Li et al [19] proposed a self-learning scheme that leverages multi-style and multi-view to reduce the vendor-style domain gap for mammography detection.…”

Section: B View-based Methodsmentioning

confidence: 99%

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Multi-View Computed Tomography Network for Osteoporosis Classification

Hwang¹,

Kim³

et al. 2023

IEEE Access

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Osteoporosis is a skeletal disease that is difficult to identify in advance of symptoms. Existing skeletal disease screening methods, such as dual-energy X-ray absorptiometry, are only used for specific purpose due to cost and safety reasons once symptoms develop. Early detection of osteopenia and osteoporosis using other modalities for relatively frequent examinations is helpful in terms of early treatment and cost. Recently, many studies have proposed deep learning-based osteoporosis diagnosis methods for various modalities and achieved outstanding results. However, these studies have limitations in clinical use because they require tedious processes, such as manually cropping a region of interest or diagnosing osteoporosis rather than osteopenia. In this study, we present a classification task for diagnosing osteopenia and osteoporosis using computed tomography (CT). Additionally, we propose a multi-view CT network (MVCTNet) that automatically classifies osteopenia and osteoporosis using two images from the original CT image. Unlike previous methods that use a single CT image as input, the MVCTNet captures various features from the images generated by our multi-view settings. The MVCTNet comprises two feature extractors and three task layers. Two feature extractors use the images as separate inputs and learn different features through dissimilarity loss. The target layers learn the target task through the features of the two feature extractors and then aggregate them. For the experiments, we use a dataset containing 2,883 patients' CT images labeled as normal, osteopenia, and osteoporosis. Additionally, we observe that the proposed method improves the performance of all experiments based on the quantitative and qualitative evaluations.

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Section: B View-based Methodsmentioning

confidence: 99%

Section: B View-based Methodsmentioning

confidence: 99%

Section: B View-based Methodsmentioning

confidence: 99%

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Multi-View Computed Tomography Network for Osteoporosis Classification

Hwang¹,

Kim³

et al. 2023

IEEE Access

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“…Therefore, to explore how contrastive learning can positively affect medical image analysis, we attempt to apply this strategy to our medical image classification task. Moreover, Chaitanya et al proposed a novel contrastive learning framework by leveraging domain-specific and problem-specific cues for medical image analysis [ 38 ]. They improved the performance of contrastive learning in dense prediction issues.…”

Section: Related Workmentioning

confidence: 99%

Triple-kernel gated attention-based multiple instance learning with contrastive learning for medical image analysis

Thiyagalingam

et al. 2023

Appl Intell

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In machine learning, multiple instance learning is a method evolved from supervised learning algorithms, which defines a “bag” as a collection of multiple examples with a wide range of applications. In this paper, we propose a novel deep multiple instance learning model for medical image analysis, called triple-kernel gated attention-based multiple instance learning with contrastive learning. It can be used to overcome the limitations of the existing multiple instance learning approaches to medical image analysis. Our model consists of four steps. i) Extracting the representations by a simple convolutional neural network using contrastive learning for training. ii) Using three different kernel functions to obtain the importance of each instance from the entire image and forming an attention map. iii) Based on the attention map, aggregating the entire image together by attention-based MIL pooling. iv) Feeding the results into the classifier for prediction. The results on different datasets demonstrate that the proposed model outperforms state-of-the-art methods on binary and weakly supervised classification tasks. It can provide more efficient classification results for various disease models and additional explanatory information.

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“…uniform voxel grids), which helps better describe delicate geometric structures. On the other hand, unlike the conventionally-used gray space [8], we leverage the contrastive learning [12] to encode the pixels to highdimensional embedding space and encourage pixels of the same labels to gather around. This helps alleviate the imaging artifacts for richer expressivity in the embedding space compared to the gray space.…”

Section: Introductionmentioning

confidence: 99%

Weakly-Supervised 3D Medical Image Segmentation using Geometric Prior and Contrastive Similarity

Du¹,

Dong²,

Xu³

et al. 2023

Preprint

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Medical image segmentation is almost the most important pre-processing procedure in computer-aided diagnosis but is also a very challenging task due to the complex shapes of segments and various artifacts caused by medical imaging, (i.e., low-contrast tissues, and non-homogenous textures). In this paper, we propose a simple yet effective segmentation framework that incorporates the geometric prior and contrastive similarity into the weakly-supervised segmentation framework in a lossbased fashion. The proposed geometric prior built on point cloud provides meticulous geometry to the weakly-supervised segmentation proposal, which serves as better supervision than the inherent property of the bounding-box annotation (i.e., height and width). Furthermore, we propose the contrastive similarity to encourage organ pixels to gather around in the contrastive embedding space, which helps better distinguish lowcontrast tissues. The proposed contrastive embedding space can make up for the poor representation of the conventionallyused gray space. Extensive experiments are conducted to verify the effectiveness and the robustness of the proposed weaklysupervised segmentation framework. The proposed framework are superior to state-of-the-art weakly-supervised methods on the following publicly accessible datasets: LiTS 2017 Challenge, KiTS 2021 Challenge and LPBA40. We also dissect our method and evaluate the performance of each component.

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Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation

Cited by 95 publications

References 34 publications

Multi-View Computed Tomography Network for Osteoporosis Classification

Multi-View Computed Tomography Network for Osteoporosis Classification

Triple-kernel gated attention-based multiple instance learning with contrastive learning for medical image analysis

Weakly-Supervised 3D Medical Image Segmentation using Geometric Prior and Contrastive Similarity

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