Ensemble 3D residual network (E3D-ResNet) for reduction of false-positive polyp detections in CT colonography

Uemura, Tomoki; Näppi, Janne J.; Lu, Huimin; Kim, Hyoungseop; Tachibana, Rie; Hironaka, Tetsuo; Yoshida, Hiroyuki

doi:10.1117/12.2512173

Cited by 3 publications

(8 citation statements)

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“…change of size, shape and location of tumours), 86,119,144,145,152–154,157 which conventional augmentation methods generally do not account for. Sometimes, geometric, deformable and intensity‐based augmentation can also be applied to the data used to train the DL‐based augmentation networks 56,91,101,138,146,152,154 or used alongside the DL‐based methods 53,83,86,87,100,119 . The majority of DL‐based augmentation approaches are based on adversarial training (including GAN‐based and other adversarial learning networks), where a discriminator network is usually used to review the generated images, and the training process iteratively bridges the gap between generated and real images.…”

Section: Discussionmentioning

confidence: 99%

“…If an image has one or more contours associated with it, the same transformation is applied to the contours. Geometric transformations are so common that they were utilised by 92 of the 93 basic augmentation studies 15–106 …”

Section: Methodsmentioning

confidence: 99%

“…The main challenges for GAN‐based image synthesis typically are maintaining training stability and generating high quality (clear, high‐resolution) images. To overcome these challenges, many previously proposed GAN variants, with improved network architecture and mathematical optimisation, have been adopted in Ref 53,87,91,131–140. as listed in Table 1.…”

Section: 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: 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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“…The nonlinear augmentation included the baseline augmentation plus 3D shifting, 3D rotation and/or application of Gaussian noise to the CT values of the VOIs. The GAN-based augmentation was based on our previously developed 3D self-attention GAN method for generating synthetic 3D polyp VOIs [15].…”

Section: Polyp Classification Studymentioning

confidence: 99%

“…Recently, several studies have explored the possibility of performing data augmentation by use of generative adversarial networks (GANs) [13,14]. In CTC, 3D GANs have been used for generation of synthetic polyps to improve the training of 3D CNNs in CADe [15]. However, the development of GANs that can generate realistic synthetic images at a high image resolution is known to suffer from various problems, such as non-convergence of the model parameters, mode collapse or training imbalance between the generator and the discriminator [5].…”

Section: Introductionmentioning

confidence: 99%

A generative flow-based model for volumetric data augmentation in 3D deep learning for computed tomographic colonography

et al. 2020

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Purpose Deep learning can be used for improving the performance of computer-aided detection (CADe) in various medical imaging tasks. However, in computed tomographic (CT) colonography, the performance is limited by the relatively small size and the variety of the available training datasets. Our purpose in this study was to develop and evaluate a flow-based generative model for performing 3D data augmentation of colorectal polyps for effective training of deep learning in CADe for CT colonography. Methods We developed a 3D-convolutional neural network (3D CNN) based on a flow-based generative model (3D Glow) for generating synthetic volumes of interest (VOIs) that has characteristics similar to those of the VOIs of its training dataset. The 3D Glow was trained to generate synthetic VOIs of polyps by use of our clinical CT colonography case collection. The evaluation was performed by use of a human observer study with three observers and by use of a CADe-based polyp classification study with a 3D DenseNet. Results The area-under-the-curve values of the receiver operating characteristic analysis of the three observers were not statistically significantly different in distinguishing between real polyps and synthetic polyps. When trained with data augmentation by 3D Glow, the 3D DenseNet yielded a statistically significantly higher polyp classification performance than when it was trained with alternative augmentation methods. Conclusion The 3D Glow-generated synthetic polyps are visually indistinguishable from real colorectal polyps. Their application to data augmentation can substantially improve the performance of 3D CNNs in CADe for CT colonography. Thus, 3D Glow is a promising method for improving the performance of deep learning in CADe for CT colonography.

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3D building fabrication with geometry and texture coordination via hybrid GAN

Shen

et al. 2020

J Ambient Intell Human Comput

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Ensemble 3D residual network (E3D-ResNet) for reduction of false-positive polyp detections in CT colonography

Cited by 3 publications

References 4 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

A generative flow-based model for volumetric data augmentation in 3D deep learning for computed tomographic colonography

3D building fabrication with geometry and texture coordination via hybrid GAN

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