# Non-Invasive to Invasive: Enhancing FFA Synthesis from CFP with a Benchmark Dataset and a Novel Network arXiv: https://arxiv.org/abs/2410.14965 ยท HTML: https://mlsanity.com/paper/2410.14965/ ## Key facts - Cited by 6 papers in the map (data through 14 Oct 2026). - Builds on 24 of its 52 references that are in the map. - First posted on arXiv 19 Oct 2024. ## Builds on (most cited first, 24 of 24) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018) - [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021) - [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016) - [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018) - [Improved Techniques for Training Score-Based Generative Models](https://mlsanity.com/paper/2006.09011/) (2020) - [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022) - [Diffusion-GAN: Training GANs with Diffusion](https://mlsanity.com/paper/2206.02262/) (2022) - [ResViT: Residual vision transformers for multi-modal medical image synthesis](https://mlsanity.com/paper/2106.16031/) (2021) - [Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping](https://mlsanity.com/paper/1809.05852/) (2018) - [Breaking the Dilemma of Medical Image-to-image Translation](https://mlsanity.com/paper/2110.06465/) (2021) - [Generative Adversarial Networks (GANs Survey): Challenges, Solutions, and Future Directions](https://mlsanity.com/paper/2005.00065/) (2020) - [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021) - [RainMamba: Enhanced Locality Learning with State Space Models for Video Deraining](https://mlsanity.com/paper/2407.21773/) (2024) - [Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation](https://mlsanity.com/paper/2303.10326/) (2023) - [Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image](https://mlsanity.com/paper/1805.07549/) (2018) - [AUFormer: Vision Transformers are Parameter-Efficient Facial Action Unit Detectors](https://mlsanity.com/paper/2403.04697/) (2024) - [VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers](https://mlsanity.com/paper/2104.06757/) (2021) - [Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals](https://mlsanity.com/paper/2309.13401/) (2023) - [A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis](https://mlsanity.com/paper/2201.07646/) (2022) - [Dynamic Interactive Relation Capturing via Scene Graph Learning for Robotic Surgical Report Generation](https://mlsanity.com/paper/2306.02651/) (2023) - [Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Dataset](https://mlsanity.com/paper/2406.13645/) (2024) - [Language-Driven Interactive Shadow Detection](https://mlsanity.com/paper/2408.08543/) (2024) - [Timeline and Boundary Guided Diffusion Network for Video Shadow Detection](https://mlsanity.com/paper/2408.11785/) (2024) ## Built on by (most cited first, 6 of 6) - [Vision-Language-Action (VLA) Models: Concepts, Progress, Applications and Challenges](https://mlsanity.com/paper/2505.04769/) (2025) - [MM-Retinal V2: Transfer an Elite Knowledge Spark into Fundus Vision-Language Pretraining](https://mlsanity.com/paper/2501.15798/) (2025) - [Continual Retinal Vision-Language Pre-training upon Incremental Imaging Modalities](https://mlsanity.com/paper/2506.19320/) (2025) - [A Survey of Multimodal Ophthalmic Diagnostics: From Task-Specific Approaches to Foundational Models](https://mlsanity.com/paper/2508.03734/) (2025) - [Native Intelligence Emerges from Large-Scale Clinical Practice: A Retinal Foundation Model with Deployment Efficiency](https://mlsanity.com/paper/2512.14499/) (2025) - [Propagating Structural Guidance: Synthesizing Fluorescein Angiography from Fundus Images and Sparse OCT Scans](https://mlsanity.com/paper/2606.16234/) (2026) ## Abstract (arXiv) Fundus imaging is a pivotal tool in ophthalmology, and different imaging modalities are characterized by their specific advantages. For example, Fundus Fluorescein Angiography (FFA) uniquely provides detailed insights into retinal vascular dynamics and pathology, surpassing Color Fundus Photographs (CFP) in detecting microvascular abnormalities and perfusion status. However, the conventional invasive FFA involves discomfort and risks due to fluorescein dye injection, and it is meaningful but challenging to synthesize FFA images from non-invasive CFP. Previous studies primarily focused on FFA synthesis in a single disease category. In this work, we explore FFA synthesis in multiple diseases by devising a Diffusion-guided generative adversarial network, which introduces an adaptive and dynamic diffusion forward process into the discriminator and adds a category-aware representation enhancer. Moreover, to facilitate this research, we collect the first multi-disease CFP and FFA paired dataset, named the Multi-disease Paired Ocular Synthesis (MPOS) dataset, with four different fundus diseases. Experimental results show that our FFA synthesis network can generate better FFA images compared to state-of-the-art methods. Furthermore, we introduce a paired-modal diagnostic network to validate the effectiveness of synthetic FFA images in the diagnosis of multiple fundus diseases, and the results show that our synthesized FFA images with the real CFP images have higher diagnosis accuracy than that of the compared FFA synthesizing methods. Our research bridges the gap between non-invasive imaging and FFA, thereby offering promising prospects to enhance ophthalmic diagnosis and patient care, with a focus on reducing harm to patients through non-invasive procedures. Our dataset and code will be released to support further research in this field (https://github.com/whq-xxh/FFA-Synthesis).