Pre-trained StyleGAN Based Data Augmentation for Small Sample Brain CT Motion Artifacts Detection

Su, Kang; Zhou, Erning; Sun, Xiaoyu; Che, Wang; Yu, Dan; Luo, Xianlu

doi:10.1007/978-3-030-65390-3_26

Cited by 10 publications

(5 citation statements)

References 8 publications

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“…However, it improved performances with respect to medical pre-training. Su et al [27] successfully used L 2 -SP to carry out the supervised adaptation of an MRI-pre-trained GAN relative to CT image generation. Unfortunately, they did not compare their results to vanilla fine-tuning.…”

Section: Discussionmentioning

confidence: 99%

“…At present, researchers using pre-trained DL models for digital histopathology can choose from various recently proposed algorithms from CV research [22][23][24]. Some initially successful applications of advanced transfer learning techniques in the medical domain can be found with respect to radiological images [25][26][27][28] or lung sound analysis [29]. However, the wider adoption of these techniques in medicine has not occurred to date.…”

Section: Introductionmentioning

confidence: 99%

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A Systematic Comparison of Task Adaptation Techniques for Digital Histopathology

Sauter,

Lodde,

Nensa

et al. 2023

Bioengineering

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Due to an insufficient amount of image annotation, artificial intelligence in computational histopathology usually relies on fine-tuning pre-trained neural networks. While vanilla fine-tuning has shown to be effective, research on computer vision has recently proposed improved algorithms, promising better accuracy. While initial studies have demonstrated the benefits of these algorithms for medical AI, in particular for radiology, there is no empirical evidence for improved accuracy in histopathology. Therefore, based on the ConvNeXt architecture, our study performs a systematic comparison of nine task adaptation techniques, namely, DELTA, L2-SP, MARS-PGM, Bi-Tuning, BSS, MultiTune, SpotTune, Co-Tuning, and vanilla fine-tuning, on five histopathological classification tasks using eight datasets. The results are based on external testing and statistical validation and reveal a multifaceted picture: some techniques are better suited for histopathology than others, but depending on the classification task, a significant relative improvement in accuracy was observed for five advanced task adaptation techniques over the control method, i.e., vanilla fine-tuning (e.g., Co-Tuning: P(≫) = 0.942, d = 2.623). Furthermore, we studied the classification accuracy for three of the nine methods with respect to the training set size (e.g., Co-Tuning: P(≫) = 0.951, γ = 0.748). Overall, our results show that the performance of advanced task adaptation techniques in histopathology is affected by influencing factors such as the specific classification task or the size of the training dataset.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Systematic Comparison of Task Adaptation Techniques for Digital Histopathology

Sauter,

Lodde,

Nensa

et al. 2023

Bioengineering

View full text Add to dashboard Cite

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“…Fetty et al [34] manipulated the latent space for high-resolution medical image synthesis via StyleGAN. Su et al [35] performed data augmentation for brain CT motion artifacts detection using StyleGAN. Hong et al [9] introduced 3D StyleGAN for volumetric medical image generation.…”

Section: Related Workmentioning

confidence: 99%

Med-cDiff: Conditional Medical Image Generation with Diffusion Models

Hung,

Zhao,

Zheng

et al. 2023

Bioengineering

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Conditional image generation plays a vital role in medical image analysis as it is effective in tasks such as super-resolution, denoising, and inpainting, among others. Diffusion models have been shown to perform at a state-of-the-art level in natural image generation, but they have not been thoroughly studied in medical image generation with specific conditions. Moreover, current medical image generation models have their own problems, limiting their usage in various medical image generation tasks. In this paper, we introduce the use of conditional Denoising Diffusion Probabilistic Models (cDDPMs) for medical image generation, which achieve state-of-the-art performance on several medical image generation tasks.

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“…Detection models can be trained using artificial artifacts. One way to generate these artifacts is by using a generative adversarial network (GAN) [19,24], but these approaches provide little to no control over the resulting artifacts. Furthermore, GANs require a large amount of training data and can lead to unreliable results (e.g., [5]).…”

Section: Related Work Motion Artifacts Are Common During Ct Acquisitionmentioning

confidence: 99%

Generating Artificial Artifacts for Motion Artifact Detection in Chest CT

Ham

Latisenko²,

Tsiaousis³

et al. 2022

Simulation and Synthesis in Medical Imaging

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Motion artifacts can have a detrimental effect on the analysis of chest CT scans, because the artifacts can mimic or obscure genuine pathological features. Localising motion artifacts in the lungs can improve diagnosis quality. The diverse appearance of artifacts requires large quantities of annotations to train a detection model, but manual annotations can be subjective, unreliable, and are labour intensive to obtain. We propose a novel method (code is available at https:// github.com/guusvanderham/artificial-motion-artifacts-for-ct) for generating artificial motion artifacts in chest CT images, based on simulated CT reconstruction. We use these artificial artifacts to train fully convolutional networks that can detect real motion artifacts in chest CT scans. We evaluate our method on scans from the public LIDC, RIDER and COVID19-CT datasets and find that it is possible to train detection models with artificially generated artifacts. Generated artifacts greatly improve performance when the availability of manually annotated scans is limited.

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Pre-trained StyleGAN Based Data Augmentation for Small Sample Brain CT Motion Artifacts Detection

Cited by 10 publications

References 8 publications

A Systematic Comparison of Task Adaptation Techniques for Digital Histopathology

A Systematic Comparison of Task Adaptation Techniques for Digital Histopathology

Med-cDiff: Conditional Medical Image Generation with Diffusion Models

Generating Artificial Artifacts for Motion Artifact Detection in Chest CT

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