Abnormality Detection in Chest X-Ray Images Using Uncertainty Prediction Autoencoders

Mao, Yifan; Xue, Fei-Fei; Wang, Ruixuan; Zhang, Jianguo; Zheng, Wei‐Shi; Li, Hongmei

doi:10.1007/978-3-030-59725-2_51

Cited by 33 publications

(18 citation statements)

References 14 publications

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“…The higher value of PSNR represents a better quality of synthetic images. PSNR is computed as shown in (10) reproduced from [60].…”

Section: ) Peak Signal-to-noise Ratio (Psnr)mentioning

confidence: 99%

“…The utility of GANs in biomedical image analysis has been extensively investigated to perform image recognition [10], image synthesis [11], image reconstruction [12], and image segmentation [13]. GANs have demonstrated a capacity to support deep learning models through the generation of synthetic images and thus enlarging the size of biomedical datasets [14] [15] [16].…”

Section: Introductionmentioning

confidence: 99%

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A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis

Saad¹,

O’Reilly²,

Rehmani³

2022

Preprint

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In biomedical image analysis, the applicability of deep learning methods is directly impacted by the quantity of image data available. This is due to deep learning models requiring large image datasets to provide high-level performance. Generative Adversarial Networks (GANs) have been widely utilized to address data limitations through the generation of synthetic biomedical images. GANs consist of two models. The generator, a model that learns how to produce synthetic images based on the feedback it receives. The discriminator, a model that classifies an image as synthetic or real and provides feedback to the generator. Throughout the training process, a GAN can experience several technical challenges that impede the generation of suitable synthetic imagery. First, the mode collapse problem whereby the generator either produces an identical image or produces a uniform image from distinct input features. Second, the non-convergence problem whereby the gradient descent optimizer fails to reach a Nash equilibrium. Thirdly, the vanishing gradient problem whereby unstable training behavior occurs due to the discriminator achieving optimal classification performance resulting in no meaningful feedback being provided to the generator. These problems result in the production of synthetic imagery that is blurry, unrealistic, and less diverse. To date, there has been no survey article outlining the impact of these technical challenges in the context of the biomedical imagery domain. This work presents a review and taxonomy based on solutions to the training problems of GANs in the biomedical imaging domain. This survey highlights important challenges and outlines future research directions about the training of GANs in the domain of biomedical imagery.

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“…The higher value of PSNR represents a better quality of synthetic images. PSNR is computed as shown in (10) reproduced from [60].…”

Section: ) Peak Signal-to-noise Ratio (Psnr)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis

Saad¹,

O’Reilly²,

Rehmani³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This study [21] presented an abnormality detection method based on an autoencoder with uncertainty prediction. This method is able to reconstruct the image with pixel-wise uncertainty prediction.…”

Section: ) Breastmentioning

confidence: 99%

Deep Learning based Anomaly Detection in Images: Insights, Challenges and Recommendations

Alloqmani¹,

Yoosef²,

Irshad³

et al. 2021

IJACSA

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Deep learning-based anomaly detection in images has recently been considered a popular research area with numerous applications worldwide. The main aim of anomaly detection (i.e., Outlier detection), is to identify data instances that deviate considerably from the majority of data instances. This paper offers a comprehensive analysis of previous works that have been proposed in the area of anomaly detection in images through deep learning generally and in the medical field specifically. Twenty studies were reviewed, and the literature selection methodology was defined based on four phases: keyword filter, publish filter, year filter, and abstract filter. In this review, we highlight the differences among the studies included by considering the following factors: methodology, dataset, preprocessing, results and limitations. Besides, we illustrate the various challenges and potential future directions relevant to anomaly detection in images.

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“…Considering the model is trained exclusively on normal observations, the adversarial generative model is able to reconstruct a normal CXR, but performs poorly on an abnormal image, thus gaining the ability to distinguish both situations based on the reconstruction differentiation. A one-class autoencoderbased approach is also implemented in Mao et al (2020), taking normal samples and outputting the reconstructed normal version of the images with an associated pixel-wise uncertainty. This way, abnormal observations in ChestX-ray14 can be identified considering the uncertaintyweighted reconstruction error as a measurement for abnormality presence.…”

Section: Abnormality Detectionmentioning

confidence: 99%

Review on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification

Rocha

Mendonça

Campilho

2021

UPjeng

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Backed by more powerful computational resources and optimized training routines, deep learning models have proven unprecedented performance and several benefits to extract information from chest X-ray data. This is one of the most common imaging exams, whose increasing demand is reflected in the aggravated radiologists’ workload. Consequently, healthcare would benefit from computer-aided diagnosis systems to prioritize certain exams and further identify possible pathologies. Pioneering work in chest X-ray analysis has focused on the identification of specific diseases, but to the best of the authors' knowledge no paper has specifically reviewed relevant work on abnormality detection and multi-label thoracic pathology classification. This paper focuses on those issues, selecting the leading chest X-ray based deep learning strategies for comparison. In addition, the paper discloses the current annotated public chest X-ray databases, covering the common thorax diseases.

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Abnormality Detection in Chest X-Ray Images Using Uncertainty Prediction Autoencoders

Cited by 33 publications

References 14 publications

A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis

A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis

Deep Learning based Anomaly Detection in Images: Insights, Challenges and Recommendations

Review on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification

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