Background And Objective: Fundus fluorescein angiography (FFA) is widely used in clinical ophthalmic diagnosis and treatment with the requirement of adverse fluorescent dyes injection. Recently, many deep Convolutional Neural Network(CNN)-based methods have been proposed to estimate FFA from color fundus (CF) images to eliminate the use of adverse fluorescent dyes. However, the robustness of these methods is affected by pathological changes.
Method: In this work, we present a CNN-based approach, lesion-aware generative adversarial networks (LA-GAN), to enhance the visual effect of lesion characteristics in the generated FFA images. First, we lead the generator notice lesion information by joint learning with lesion region segmentation. A new hierarchical correlation multi-task framework for high-resolution images is designed. Second, to enhance the visual contrast between normal regions and lesion regions, a newly designed region-level adversarial loss is used rather than the image-level adversarial loss. The code is publicly available at: https://github.com/nicetomeetu21/LA-GAN.
Results: The effectiveness of LA-Net has been verified in data with branch retinal vein occlusion. The proposed model reported as measures of generation performance a mean structural similarity (SSIM) of 0.536, mean learned perceptual image patch similarity (LPIPS) 0.312, outperforming other FFA generation and general image generation methods. Further, due to the proposed multi-task learning framework, the lesion-region segmentation performance was further reported as the mean Dice increased from 0.714 to 0.797 and the mean accuracy increased from 0.873 to 0.905, outperforming general single-task image segmentation methods.
Conclusions: The results show that the visual effect of lesion characteristics can be improved by employing the region-level adversarial loss and the hierarchical correlation multi-task framework respectively. Based on the results of comparison with the state-of-the-art methods, LA-GAN is not only effective for CF-to-FFA translation, but also effective for lesion-region segmentation. Thus, it may be used for various image translation and lesion segmentation tasks in future research.
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http://dx.doi.org/10.1016/j.cmpb.2022.107306 | DOI Listing |
Alzheimers Dement
December 2024
Massachusetts Institute of Technology, Cambridge, MA, USA
Background: Speech is a predominant mode of human communication. Speech digital recordings are inexpensive to record and contain rich health related information. Deep learning, a key method, excels in detecting intricate patterns, however, it requires substantial training data.
View Article and Find Full Text PDFSci Rep
January 2025
School of Mechanical, Electrical, and Information Engineering, Putian University, Putian, 351100, China.
Noise label learning has attracted considerable attention owing to its ability to leverage large amounts of inexpensive and imprecise data. Sharpness aware minimization (SAM) has shown effective improvements in the generalization performance in the presence of noisy labels by introducing adversarial weight perturbations in the model parameter space. However, our experimental observations have shown that the SAM generalization bottleneck primarily stems from the difficulty of finding the correct adversarial perturbation amidst the noisy data.
View Article and Find Full Text PDFSci Rep
January 2025
NeMO Lab, ASST GOM Niguarda Cà Granda Hospital, Milan, Italy.
Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that can result in a progressive loss of speech due to bulbar dysfunction, which can have significant negative impact on the patient's mental well-being. Alternative Augmentative Communication (AAC) strategies based on synthetic voices have been shown to assist patients in maintaining communication and improving their Quality of Life (QoL). However, such synthetic voices are often perceived as impersonal and fail to capture the unique voice and identity of the patient.
View Article and Find Full Text PDFInterdiscip Sci
January 2025
College of Science, Dalian Jiaotong University, Dalian, 116028, China.
Accurate prediction of drug-drug interaction (DDI) is essential to improve clinical efficacy, avoid adverse effects of drug combination therapy, and enhance drug safety. Recently researchers have developed several computer-aided methods for DDI prediction. However, these methods lack the substructural features that are critical to drug interactions and are not effective in generalizing across domains and different distribution data.
View Article and Find Full Text PDFPLoS One
January 2025
School of Electrical Engineering, Zhejiang University, Hangzhou, China.
Adversarial training has become a primary method for enhancing the robustness of deep learning models. In recent years, fast adversarial training methods have gained widespread attention due to their lower computational cost. However, since fast adversarial training uses single-step adversarial attacks instead of multi-step attacks, the generated adversarial examples lack diversity, making models prone to catastrophic overfitting and loss of robustness.
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