Towards Practical Unsupervised Anomaly Detection on Retinal Images

Ouardini, Khalil Ouardini; Yang, Huijuan; Unnikrishnan, Balagopal; Romain, Manon; Garcin, Camille; Zenati, Houssam; Campbell, J. Peter; Chiang, Michael F.; Kalpathy–Cramer, Jayashree; Chandrasekhar, Vijay; Krishnaswamy, Pavitra; Foo, Chuan-Sheng

doi:10.1007/978-3-030-33391-1_26

Cited by 32 publications

(30 citation statements)

References 12 publications

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“…Retinal fundus photography is simpler to operate and more cost-effective than OCT, which renders it suitable for early screening of ocular disease [ 40 ]. Thus far, only Ouardini et al [ 34 ] have used AD with regard to color retinal fundus images; however, the 2 data sets they used were small, and only 1 type of fundus abnormality (retinopathy of prematurity or DR) was included in each data set. In our study, we developed an AD model based on 4 large-scale data sets derived from clinical or population screening and conducted external validation on 4 independent data sets, which ensured the robustness and generalizability of the model.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, a series of generative adversarial network (GAN)–based AD methods were proposed for OCT AD, which demonstrated excellent performance [ 31 - 33 ]. To date, only 1 study has adopted the isolation forest AD algorithm to detect ocular diseases on the basis of small-scale data sets of color retinal fundus images [ 34 ]. The area under the receiver operating characteristic curve (AUC) of the model for detecting premature retinopathy and DR were 0.770 and 0.745, respectively, which do yet meet clinical requirements.…”

Section: Introductionmentioning

confidence: 99%

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Application of an Anomaly Detection Model to Screen for Ocular Diseases Using Color Retinal Fundus Images: Design and Evaluation Study

Han¹,

Li²,

Liu³

et al. 2021

J Med Internet Res

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Background The supervised deep learning approach provides state-of-the-art performance in a variety of fundus image classification tasks, but it is not applicable for screening tasks with numerous or unknown disease types. The unsupervised anomaly detection (AD) approach, which needs only normal samples to develop a model, may be a workable and cost-saving method of screening for ocular diseases. Objective This study aimed to develop and evaluate an AD model for detecting ocular diseases on the basis of color fundus images. Methods A generative adversarial network–based AD method for detecting possible ocular diseases was developed and evaluated using 90,499 retinal fundus images derived from 4 large-scale real-world data sets. Four other independent external test sets were used for external testing and further analysis of the model’s performance in detecting 6 common ocular diseases (diabetic retinopathy [DR], glaucoma, cataract, age-related macular degeneration, hypertensive retinopathy [HR], and myopia), DR of different severity levels, and 36 categories of abnormal fundus images. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the model’s performance were calculated and presented. Results Our model achieved an AUC of 0.896 with 82.69% sensitivity and 82.63% specificity in detecting abnormal fundus images in the internal test set, and it achieved an AUC of 0.900 with 83.25% sensitivity and 85.19% specificity in 1 external proprietary data set. In the detection of 6 common ocular diseases, the AUCs for DR, glaucoma, cataract, AMD, HR, and myopia were 0.891, 0.916, 0.912, 0.867, 0.895, and 0.961, respectively. Moreover, the AD model had an AUC of 0.868 for detecting any DR, 0.908 for detecting referable DR, and 0.926 for detecting vision-threatening DR. Conclusions The AD approach achieved high sensitivity and specificity in detecting ocular diseases on the basis of fundus images, which implies that this model might be an efficient and economical tool for optimizing current clinical pathways for ophthalmologists. Future studies are required to evaluate the practical applicability of the AD approach in ocular disease screening.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of an Anomaly Detection Model to Screen for Ocular Diseases Using Color Retinal Fundus Images: Design and Evaluation Study

Han¹,

Li²,

Liu³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…This study [27] proposed a transfer-learning-based approach for unsupervised anomaly detection. The methodology used a convolutional neural network as a feature extractor and Isolation Forest anomaly detection method as a classification.…”

Section: ) Eyementioning

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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“…In [14]- [16] denoising auto-encoders and GANs were used to adversely learn latent representations for one-class novelty detection. A deep convolution neural network while utilizing ImageNet for feature extraction was used with transfer-level learning for an unsupervised anomaly detection in medical images [17]. In [18] a framework was proposed utilizing a deep auto-encoder as a parametric density estimator and through autoregression it learned the probability distribution of its underlying latent representations without any prior assumption about the nature of novelties.…”

Section: Related Literaturementioning

confidence: 99%

“…However, in the proposed anomaly detection algorithm we have used expectation maximization (EM) optimization to evaluate these parameters to perform clustering in dictionary of visual words. The EM algorithm returns optimum values of these parameters which maximises the log likelihood function given in (17).…”

Section: ) Measurement Modelmentioning

confidence: 99%

Anomaly Detection With Particle Filtering for Online Video Surveillance

et al. 2021

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With growing security threats, many online and offine frameworks have been proposed for anomaly detection in video sequences. However, existing online anomaly detection techniques are either computationally very expensive or lack desirable accuracy. This research work proposes a novel particle filtering based framework for online anomaly detection which detects video frames with anomalous activities based upon the posterior probability of activities in a video sequence. The proposed method also detects anomalous regions in anomalous video frames. We propose novel prediction and measurement models to accurately detect anomalous video frames and anomalous regions in video frames. Novel prediction model for particle prediction and likelihood model for assigning weights to these particles are proposed. These models efficiently utilise variable sized cell structure which creates variable sized subregions of scenes in video frames. Furthermore, they efficiently extract and utilise information from the video frame in the form of size, motion and location features. The proposed framework is tested on UCSD and LIVE datasets and compared with the existing state-of-the-art algorithms in the literature. The proposed anomaly detection algorithm outperforms the state-of-the art algorithms in terms of reduced Equal Error Rate (EER) with comparatively lesser processing time. INDEX TERMS Video anomaly detection, online framework, particle filtering, inference mechanism.

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Towards Practical Unsupervised Anomaly Detection on Retinal Images

Cited by 32 publications

References 12 publications

Application of an Anomaly Detection Model to Screen for Ocular Diseases Using Color Retinal Fundus Images: Design and Evaluation Study

Application of an Anomaly Detection Model to Screen for Ocular Diseases Using Color Retinal Fundus Images: Design and Evaluation Study

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

Anomaly Detection With Particle Filtering for Online Video Surveillance

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