Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Bergmann, Paul; Löwe, Sindy; Fauser, Michael; Sattlegger, David; Steger, Carsten

doi:10.5220/0007364503720380

Cited by 296 publications

(242 citation statements)

References 17 publications

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“…propose a combination of pixel‐wise reconstruction error based on an

L^{p}

distance with an explicit sparsity term and an adversarial loss to improve the quality of the reconstructed images during the training phase, therefore obtaining a better representation of the appearance of normal data. Bergmann et al . suggest to replace the pixel‐wise reconstruction error based on an

L^{p}

distance with overall image structural similarity index (SSIM) …”

Section: Discussionmentioning

confidence: 99%

Unsupervised abnormality detection through mixed structure regularization (MSR) in deep sparse autoencoders

2019

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Purpose The purpose of this study is to introduce and evaluate the mixed structure regularization (MSR) approach for a deep sparse autoencoder aimed at unsupervised abnormality detection in medical images. Unsupervised abnormality detection based on identifying outliers using deep sparse autoencoders is a very appealing approach for computer‐aided detection systems as it requires only healthy data for training rather than expert annotated abnormality. However, regularization is required to avoid overfitting of the network to the training data. Methods We used coronary computed tomography angiography (CCTA) datasets of 90 subjects with expert annotated centerlines. We segmented coronary lumen and wall using an automatic algorithm with manual corrections where required. We defined normal coronary cross section as cross sections with a ratio between lumen and wall areas larger than 0.8. We divided the datasets into training, validation, and testing groups in a tenfold cross‐validation scheme. We trained a deep sparse overcomplete autoencoder model for normality modeling with random structure and noise augmentation. We assessed the performance of our deep sparse autoencoder with MSR without denoising (SAE‐MSR) and with denoising (SDAE‐MSR) in comparison to deep sparse autoencoder (SAE), and deep sparse denoising autoencoder (SDAE) models in the task of detecting coronary artery disease from CCTA data on the test group. Results The SDAE‐MSR achieved the best aggregated area under the curve (AUC) with a 20% improvement and the best aggregated Average Precision (AP) with a 30% improvement upon the SAE and SDAE (AUC: 0.78 to 0.94, AP: 0.66 to 0.86) in distinguishing between coronary cross sections with mild stenosis (stenosis grade < 0.3) and coronary cross sections with severe stenosis (stenosis grade > 0.7). The improvements were statistically significant (Mann–Whitney U‐test, P < 0.001). Similarly, The SDAE‐MSR achieved the best aggregated AUC (AP) with an 18% (18%) improvement upon the SAE and SDAE (AUC: 0.71 to 0.84, AP: 0.68 to 0.80). The improvements were statistically significant (Mann–Whitney U‐test, P < 0.05). Conclusion Deep sparse autoencoders with MSR in addition to explicit sparsity regularization term and stochastic corruption of the input data with Gaussian noise have the potential to improve unsupervised abnormality detection using deep‐learning compared to common deep autoencoders.

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“…propose a combination of pixel‐wise reconstruction error based on an

L^{p}

L^{p}

distance with overall image structural similarity index (SSIM) …”

Section: Discussionmentioning

confidence: 99%

Unsupervised abnormality detection through mixed structure regularization (MSR) in deep sparse autoencoders

2019

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“…Bergmann et al [7] used perceptual loss function to get better performance of the autoencoder in defect detection. They note that existing methods lead to large residuals in edge regions that have slight localization inaccuracies.…”

Section: Related Workmentioning

confidence: 99%

Fabric Defect Detection System Using Stacked Convolutional Denoising Auto-Encoders Trained with Synthetic Defect Data

Han

2020

Applied Sciences

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As defect detection using machine vision is diversifying and expanding, approaches using deep learning are increasing. Recently, there have been much research for detecting and classifying defects using image segmentation, image detection, and image classification. These methods are effective but require a large number of actual defect data. However, it is very difficult to get a large amount of actual defect data in industrial areas. To overcome this problem, we propose a method for defect detection using stacked convolutional autoencoders. The autoencoders we proposed are trained by using only non-defect data and synthetic defect data generated by using the characteristics of defect based on the knowledge of the experts. A key advantage of our approach is that actual defect data is not required, and we verified that the performance is comparable to the systems trained using real defect data.

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“…Most common methods calculate the reconstruction error as mean-squared error(MSE). Bergmann et al considered the structural similarity measure instead of the MSE [16]. The structural similarity measure helps capture the interdependencies of adjacent pixels because it considers three different statistical measures: luminance, contrast, and structure.…”

Section: Related Workmentioning

confidence: 99%

Latent Feature Decentralization Loss for One-Class Anomaly Detection

Hong

Choe

2020

IEEE Access

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Anomaly detection is essential for many real-world applications, such as video surveillance, disease diagnosis, and visual inspection. With the development of neural networks, many neural networks have been used for anomaly detection by learning the distribution of normal data. However, they are vulnerable to distinguishing abnormalities when the normal and abnormal images are not significantly different. To mitigate this problem, we propose a novel loss function for one-class anomaly detection: decentralization loss. The main goal of the proposed method is to cause the latent feature of the encoder to disperse over the manifold space, such that the decoder can generate images similar to those in a normal class for any input. To this end, a decentralization term designed based on the dispersion measure for latent vectors is also added to the existing mean-squared error loss. To design a general solution for various datasets, we restrict the latent space by designing a decentralization loss term-based upper bound of the dispersion measure. As intended, a model trained with the proposed decentralization loss function disperses vectors on the manifold space and generates constant images. Consequently, the reconstruction error increases when the given test image is unknown. Experiments conducted on various datasets verify that the proposed function improves detection performance improved by about 1 % while reducing training time by 48 %, without any structural changes in the conventional autoencoder.

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Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Cited by 296 publications

References 17 publications

Unsupervised abnormality detection through mixed structure regularization (MSR) in deep sparse autoencoders

Unsupervised abnormality detection through mixed structure regularization (MSR) in deep sparse autoencoders

Fabric Defect Detection System Using Stacked Convolutional Denoising Auto-Encoders Trained with Synthetic Defect Data

Latent Feature Decentralization Loss for One-Class Anomaly Detection

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