An Unsupervised Adversarial Learning Approach to Fundus Fluorescein Angiography Image Synthesis for Leakage Detection

Li, Wanyue; He, Yi; Wang, Jing; Kong, Weijing; Chen, Yiwei; Shi, Guohua

doi:10.1007/978-3-030-59520-3_15

Cited by 2 publications

(10 citation statements)

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“…In this section, we compared the proposed method with the one by Li et al, 8 which is also the baseline model of the proposed method. Starting from this baseline model (CycleGAN), which consists of generator G A2N without AM loss and CBAM, we also conducted an ablation study to validate the inclusion of AM loss and CBAM.…”

Section: Resultsmentioning

confidence: 99%

“…The proposed model was also tested on two publicly available data sets (data sets from Zhao et al 2 and Rabbani et al 4 ) and compared with the methods by Zhao et al, 2 Rabbani et al, 4 and Li et al 8 Zhao et al's data set contains 30 abnormal FA images (20 large focal and 10 punctate focal) with signs of malarial retinopathy (MR) on admission. Figure 8 shows that the leakage detection results of the proposed method are closer to the expert's annotation, and Figure 9 illustrates the better performance of the proposed method on leakage detection when compared with Li et al's method.…”

Section: Resultsmentioning

confidence: 99%

“…The main idea of the proposed method is similar to the study proposed by Li et al, 8 that is, to train a model that can generate a normal-looking FA image from the input abnormal image and then detect the leakage by calculating the difference between the abnormal and generated normal images.…”

Section: Methodsmentioning

confidence: 99%

“…For a fair comparison with other state-of-the-art methods, the testing results were all evaluated with the criteria of sensitivity (Sen), specificity (Spe), accuracy (Acc), area under the curve (AUC), and dice coefficient (DC), as in the existing leakage detection works. 2 , 4 , 8 These metrics can be calculated as

where TP , TN , FP , and FN represent the number of true positives (correctly identified leakage pixels or regions), true negatives (correctly identified background pixels or regions), false positives (incorrectly identified leakage pixels or regions), and false negatives (incorrectly identified background pixels or regions), respectively. A indicates the ground truth regions, B indicates the segmented regions, and |A∩B| denotes the number of pixels in the intersecting region between A and B .…”

Section: Methodsmentioning

confidence: 99%

“…However, these methods are all supervised and require a large amount of training data derived from manual annotation, which makes their performances inherently dependent on the quality of the annotations. To solve this problem, Li et al 8 proposed an unsupervised learning-based fluorescence leakage detection method. However, this method cannot focus solely on the learning of leakage areas and leads to false leakage detection.…”

Section: Introductionmentioning

confidence: 99%

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A Weakly Supervised Deep Learning Approach for Leakage Detection in Fluorescein Angiography Images

Fang

Wang

et al. 2022

Trans. Vis. Sci. Tech.

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Purpose The purpose of this study was to design an automated algorithm that can detect fluorescence leakage accurately and quickly without the use of a large amount of labeled data. Methods A weakly supervised learning-based method was proposed to detect fluorescein leakage without the need for manual annotation of leakage areas. To enhance the representation of the network, a residual attention module (RAM) was designed as the core component of the proposed generator. Moreover, class activation maps (CAMs) were used to define a novel anomaly mask loss to facilitate more accurate learning of leakage areas. In addition, sensitivity, specificity, accuracy, area under the curve (AUC), and dice coefficient (DC) were used to evaluate the performance of the methods. Results The proposed method reached a sensitivity of 0.73 ± 0.04, a specificity of 0.97 ± 0.03, an accuracy of 0.95 ± 0.05, an AUC of 0.86 ± 0.04, and a DC of 0.87 ± 0.01 on the HRA data set; a sensitivity of 0.91 ± 0.02, a specificity of 0.97 ± 0.02, an accuracy of 0.96 ± 0.03, an AUC of 0.94 ± 0.02, and a DC of 0.85 ± 0.03 on Zhao's publicly available data set; and a sensitivity of 0.71 ± 0.04, a specificity of 0.99 ± 0.06, an accuracy of 0.87 ± 0.06, an AUC of 0.85 ± 0.02, and a DC of 0.78 ± 0.04 on Rabbani's publicly available data set. Conclusions The experimental results showed that the proposed method achieves better performance on fluorescence leakage detection and can detect one image within 1 second and thus has great potential value for clinical diagnosis and treatment of retina-related diseases, such as diabetic retinopathy and malarial retinopathy. Translational Relevance The proposed weakly supervised learning-based method that automates the detection of fluorescence leakage can facilitate the assessment of retinal-related diseases.

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Section: Resultsmentioning

confidence: 99%