Automatic Data Augmentation Via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation

Qin, Tiexin; Wang, Ziyuan; He, Kelei; Shi, Yinghuan; Gao, Yang; Shen, Dinggang

doi:10.1109/icassp40776.2020.9053403

Cited by 18 publications

(14 citation statements)

References 16 publications

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“…10 Many articles demonstrated the effectiveness of the DL-based augmentation by showing a higher performance improvement on real testing data for classification or segmentation tasks compared with no augmentation, basic geometric or deformable augmentation approaches. 56,86,91,100,101,119,[132][133][134]136,[143][144][145][146]152,[154][155][156][157][158]164,176,177 Another way of ensuring the realism of the synthetic data is to have the generated data reviewed by clinicians 86,101,119,133,135,136,[144][145][146] ; however, this approach could be resource intensive, time consuming and difficult to scale. 10 DL-based augmentation can also be challenged by its high computational cost.…”

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…This approach can synthesise both malignant and benign lung nodules to alleviate class imbalance and improve malignant nodule classification performance. Instead of using generative model to produce synthetic images, Qin et al 164 and Xu et al 56 proposed to augment data though embedding spatial transformations in a DL network which is combined with a segmentation network in an end-toend fashion. Apart from the common image classification and segmentation tasks, Tanno et al 165 adopted variational dropout 166 as a form of data augmentation for uncertainty modelling in DL for image enhancement.…”

Section: Other Dl-based Augmentation Methodsmentioning

confidence: 99%

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A review of medical image data augmentation techniques for deep learning applications

et al. 2021

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Summary Research in artificial intelligence for radiology and radiotherapy has recently become increasingly reliant on the use of deep learning‐based algorithms. While the performance of the models which these algorithms produce can significantly outperform more traditional machine learning methods, they do rely on larger datasets being available for training. To address this issue, data augmentation has become a popular method for increasing the size of a training dataset, particularly in fields where large datasets aren’t typically available, which is often the case when working with medical images. Data augmentation aims to generate additional data which is used to train the model and has been shown to improve performance when validated on a separate unseen dataset. This approach has become commonplace so to help understand the types of data augmentation techniques used in state‐of‐the‐art deep learning models, we conducted a systematic review of the literature where data augmentation was utilised on medical images (limited to CT and MRI) to train a deep learning model. Articles were categorised into basic, deformable, deep learning or other data augmentation techniques. As artificial intelligence models trained using augmented data make their way into the clinic, this review aims to give an insight to these techniques and confidence in the validity of the models produced.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Other Dl-based Augmentation Methodsmentioning

confidence: 99%

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A review of medical image data augmentation techniques for deep learning applications

et al. 2021

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“…Instead of being directly involved in the segmentation process, RL agents can be applied to optimize the existing medical image segmentation pipelines. 38 , 39 , 40 Bae et al. 38 used RL as the controller to automate the searching process of optimal neural architecture.…”

Section: Rl In Medical Image Analysismentioning

confidence: 99%

Reinforcement learning in medical image analysis: Concepts, applications, challenges, and future directions

Zhang

Luke

et al. 2023

J Applied Clin Med Phys

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Motivation Medical image analysis involves a series of tasks used to assist physicians in qualitative and quantitative analyses of lesions or anatomical structures which can significantly improve the accuracy and reliability of medical diagnoses and prognoses. Traditionally, these tedious tasks were finished by experienced physicians or medical physicists and were marred with two major problems, low efficiency and bias. In the past decade, many machine learning methods have been applied to accelerate and automate the image analysis process. Compared to the enormous deployments of supervised and unsupervised learning models, attempts to use reinforcement learning in medical image analysis are still scarce. We hope that this review article could serve as the stepping stone for related research in the future. Significance We found that although reinforcement learning has gradually gained momentum in recent years, many researchers in the medical analysis field still find it hard to understand and deploy in clinical settings. One possible cause is a lack of well‐organized review articles intended for readers without professional computer science backgrounds. Rather than to provide a comprehensive list of all reinforcement learning models applied in medical image analysis, the aim of this review is to help the readers formulate and solve their medical image analysis research through the lens of reinforcement learning. Approach & Results We selected published articles from Google Scholar and PubMed. Considering the scarcity of related articles, we also included some outstanding newest preprints. The papers were carefully reviewed and categorized according to the type of image analysis task. In this article, we first reviewed the basic concepts and popular models of reinforcement learning. Then, we explored the applications of reinforcement learning models in medical image analysis. Finally, we concluded the article by discussing the reviewed reinforcement learning approaches’ limitations and possible future improvements.

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“…For that, the researchers started using DL and DRL techniques for data augmentation especially for medical imaging that suffer from the lack of data for many diseases. For example, DRL is used for creating new images to be used for training like in [154]. While the authors proposed a DRL architecture for kidney Tumor segmentation.…”

Section: Data Augmentationmentioning

confidence: 99%

Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches

Elharrouss¹,

Akbari²,

Almaadeed³

et al. 2022

Preprint

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To understand the real world using various types of data, Artificial Intelligence (AI) is the most used technique nowadays. While finding the pattern within the analyzed data represents the main task. This is performed by extracting representative features step, which is proceeded using the statistical algorithms or using some specific filters. However, the selection of useful features from large-scale data represented a crucial challenge. Now, with the development of convolution neural networks (CNNs), the feature extraction operation has become more automatic and easier. CNNs allow to work on large-scale size of data, as well as cover different scenarios for a specific task. For computer vision tasks, convolutional networks are used to extract features also for the other parts of a deep learning model. The selection of a suitable network for feature extraction or the other parts of a DL model is not random work. So, the implementation of such a model can be related to the target task as well as the computational complexity of it. Many networks have been proposed and become the famous networks used for any DL models in any AI task. These networks are exploited for feature extraction or at the beginning of any DL model which is named backbones. A backbone is a known network trained in many other tasks before and demonstrates its effectiveness. In this paper, an overview of the existing backbones, e.g. VGGs, ResNets, DenseNet, etc, is given with a detailed description. Also, a couple of computer vision tasks are discussed by providing a review of each task regarding the backbones used. In addition, a comparison in terms of performance is also provided, based on the backbone used for each task.

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Automatic Data Augmentation Via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation

Cited by 18 publications

References 16 publications

A review of medical image data augmentation techniques for deep learning applications

A review of medical image data augmentation techniques for deep learning applications

Reinforcement learning in medical image analysis: Concepts, applications, challenges, and future directions

Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches

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