Abstract：Macular edema, a prevalent ocular complication observed in various retinal diseases, can lead to significant vision loss or blindness, necessitating accurate and timely diagnosis. Despite the potential of deep learning for segmentation of macular edema, challenges persist in accurately identifying lesion boundaries, especially in low-contrast and noisy regions, and in distinguishing between Inner Retinal Fluid (IRF), Sub-Retinal Fluid (SRF), and Pigment Epithelial Detachment (PED) lesions. To address these challenges, we present a novel approach, termed Semantic Uncertainty Guided Cross-Transformer Network (SuGCTNet), for the simultaneous segmentation of multi-class macular edema. Our proposed method comprises two key components, the semantic uncertainty guided attention module (S

Abstract：Multispectral imaging, a non-invasive technique to measure retinal oxygen saturation levels, is limited due to its time-consuming and expensive nature. In this paper, we present R3ST (Retinex-based Semantic Spectral Separation Transformer), an innovative end-to-end method for RGB fundus image spectral reconstruction. Our proposed model leverages RGB images to reconstruct multispectral images, making retinal oximetry more accessible for diagnostics.Existing spectral reconstruction methods face challenges in achieving high accuracy and generalizing across camera sources, affecting diagnostic results. R3ST addresses these issues through an unsupervised semantic spectral separation module that strives to encourage the separate reconstruction of pixels with different semantics as much