Few-shot learning for deformable image registration in 4DCT images

Chi, Weicheng; Xiang, Zhiming; Guo, Fen

doi:10.1259/bjr.20210819

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…They used a popular fewshot learning model, namely, Siamese networks (Koch et al 2015), which distinguished the different classes by ranking the similarity between input images. Other examples include the use of few-shot learning for deformable image registration and motion tracking in 4DCTs (Fechter and Baltas 2020, Chi et al 2022.…”

Section: Other Trendsmentioning

confidence: 99%

Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency

Montero¹,

Bibal²,

Huet³

et al. 2022

Phys. Med. Biol.

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The interest for machine learning (ML) has grown tremendously in recent years, partly due to the performance leap that occurred with new techniques of deep learning, convolutional neural networks for images, increased computational power, and wider availability of large data sets. Most fields of medicine follow that popular trend and, notably, radiation oncology is one of those that are at the forefront, with already a long tradition in using digital images and fully computerized workflows. ML models are driven by data, and in contrast with many statistical or physical models, they can be very large and complex, with countless generic parameters. This inevitably raises two questions, namely, the tight dependence between the models and the data sets that feed them, and the interpretability of the models, which scales with its complexity. Any problems in the data used to train the model will be later reflected in their performance. This, together with the low interpretability of ML models, makes their implementation into the clinical workflow particularly difficult. Building tools for risk assessment and quality assurance of ML models must involve then two main points: interpretability and data-model dependency. After a joint introduction of both radiation oncology and ML, this paper reviews the main risks and current solutions when applying the latter to workflows in the former. Risks associated with data and models, as well as their interaction, are detailed. Next, the core concepts of interpretability, explainability, and data-model dependency are formally defined and illustrated with examples. Afterwards, a broad discussion goes through key applications of ML in workflows of radiation oncology as well as vendors’ perspectives for the clinical implementation of ML.

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Section: Other Trendsmentioning

confidence: 99%

Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency

Montero¹,

Bibal²,

Huet³

et al. 2022

Phys. Med. Biol.

View full text Add to dashboard Cite

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“…Deep learning techniques have achieved great success, especially in the field of image processing, such as image classification (Bateni et al, 2022 ), image registration (Chi et al, 2022 ), and image segmentation (Gao H. et al, 2022 ). Traditional deep learning is highly data-dependent, which requires training a lot of data to produce high performance.…”

Section: Introductionmentioning

confidence: 99%

Learning with few samples in deep learning for image classification, a mini-review

Zhang

Liu

2023

Front. Comput. Neurosci.

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Deep learning has achieved enormous success in various computer tasks. The excellent performance depends heavily on adequate training datasets, however, it is difficult to obtain abundant samples in practical applications. Few-shot learning is proposed to address the data limitation problem in the training process, which can perform rapid learning with few samples by utilizing prior knowledge. In this paper, we focus on few-shot classification to conduct a survey about the recent methods. First, we elaborate on the definition of the few-shot classification problem. Then we propose a newly organized taxonomy, discuss the application scenarios in which each method is effective, and compare the pros and cons of different methods. We classify few-shot image classification methods from four perspectives: (i) Data augmentation, which contains sample-level and task-level data augmentation. (ii) Metric-based method, which analyzes both feature embedding and metric function. (iii) Optimization method, which is compared from the aspects of self-learning and mutual learning. (iv) Model-based method, which is discussed from the perspectives of memory-based, rapid adaptation and multi-task learning. Finally, we conduct the conclusion and prospect of this paper.

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