AWeakly Supervised Deep Learning Approach for Leakage Detection in Fluorescein Angiography Images

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: Aweakly 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.