Self-similarity-based super-resolution of photoacoustic angiography from hand-drawn doodles
This work addresses generalization issues in PAA super-resolution for disease diagnosis, particularly in continuous monitoring tasks, but it is incremental as it builds on existing deep learning and image generation techniques.
The paper tackles the problem of limited training samples for super-resolution photoacoustic angiography (PAA) by proposing a method that uses forged PAA images generated from hand-drawn curves to train a self-similarity-based model, resulting in improved performance over models trained with authentic images in both original-domain and cross-domain tests.
Deep-learning-based super-resolution photoacoustic angiography (PAA) is a powerful tool that restores blood vessel images from under-sampled images to facilitate disease diagnosis. Nonetheless, due to the scarcity of training samples, PAA super-resolution models often exhibit inadequate generalization capabilities, particularly in the context of continuous monitoring tasks. To address this challenge, we propose a novel approach that employs a super-resolution PAA method trained with forged PAA images. We start by generating realistic PAA images of human lips from hand-drawn curves using a diffusion-based image generation model. Subsequently, we train a self-similarity-based super-resolution model with these forged PAA images. Experimental results show that our method outperforms the super-resolution model trained with authentic PAA images in both original-domain and cross-domain tests. Specially, our approach boosts the quality of super-resolution reconstruction using the images forged by the deep learning model, indicating that the collaboration between deep learning models can facilitate generalization, despite limited initial dataset. This approach shows promising potential for exploring zero-shot learning neural networks for vision tasks.