Symmetrical Supervision with Transformer for Few-shot Medical Image Segmentation

Niu, Yao; Luo, Zhiming; Lian, Sheng; Li, Lei; Li, Shaozi; Song, Haixin

doi:10.1109/bibm55620.2022.9995238

Cited by 3 publications

References 17 publications

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A review of self‐supervised, generative, and few‐shot deep learning methods for data‐limited magnetic resonance imaging segmentation

Liu,

Kainth,

Zhou

et al. 2024

NMR in Biomedicine

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Magnetic resonance imaging (MRI) is a ubiquitous medical imaging technology with applications in disease diagnostics, intervention, and treatment planning. Accurate MRI segmentation is critical for diagnosing abnormalities, monitoring diseases, and deciding on a course of treatment. With the advent of advanced deep learning frameworks, fully automated and accurate MRI segmentation is advancing. Traditional supervised deep learning techniques have advanced tremendously, reaching clinical‐level accuracy in the field of segmentation. However, these algorithms still require a large amount of annotated data, which is oftentimes unavailable or impractical. One way to circumvent this issue is to utilize algorithms that exploit a limited amount of labeled data. This paper aims to review such state‐of‐the‐art algorithms that use a limited number of annotated samples. We explain the fundamental principles of self‐supervised learning, generative models, few‐shot learning, and semi‐supervised learning and summarize their applications in cardiac, abdomen, and brain MRI segmentation. Throughout this review, we highlight algorithms that can be employed based on the quantity of annotated data available. We also present a comprehensive list of notable publicly available MRI segmentation datasets. To conclude, we discuss possible future directions of the field—including emerging algorithms, such as contrastive language‐image pretraining, and potential combinations across the methods discussed—that can further increase the efficacy of image segmentation with limited labels.

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A review of self‐supervised, generative, and few‐shot deep learning methods for data‐limited magnetic resonance imaging segmentation

Liu,

Kainth,

Zhou

et al. 2024

NMR in Biomedicine

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Few Shot Learning for Medical Imaging: A Review of Categorized Images

Imran,

Asad,

Abdullah

et al. 2023

2023 IEEE 7th Conference on Information and Communication Technology (CICT)

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Application of visual transformer in renal image analysis

Yin,

Tang,

Weng

2024

BioMed Eng OnLine

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Deep Self-Attention Network (Transformer) is an encoder–decoder architectural model that excels in establishing long-distance dependencies and is first applied in natural language processing. Due to its complementary nature with the inductive bias of convolutional neural network (CNN), Transformer has been gradually applied to medical image processing, including kidney image processing. It has become a hot research topic in recent years. To further explore new ideas and directions in the field of renal image processing, this paper outlines the characteristics of the Transformer network model and summarizes the application of the Transformer-based model in renal image segmentation, classification, detection, electronic medical records, and decision-making systems, and compared with CNN-based renal image processing algorithm, analyzing the advantages and disadvantages of this technique in renal image processing. In addition, this paper gives an outlook on the development trend of Transformer in renal image processing, which provides a valuable reference for a lot of renal image analysis.

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Symmetrical Supervision with Transformer for Few-shot Medical Image Segmentation

Cited by 3 publications

References 17 publications

A review of self‐supervised, generative, and few‐shot deep learning methods for data‐limited magnetic resonance imaging segmentation

A review of self‐supervised, generative, and few‐shot deep learning methods for data‐limited magnetic resonance imaging segmentation

Few Shot Learning for Medical Imaging: A Review of Categorized Images

Application of visual transformer in renal image analysis

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