Multi-disease Detection in Retinal Images Guided by Disease Causal Estimation

There have been significant advancements in analyzing retinal images for the diagnosis of eye diseases and other systemic conditions. However, a key challenge is multi-disease detection, particularly in addressing the demands of real-world applications where a patient may have more than one condition. To address this challenge, this study introduces a novel end-to-end approach to multi-disease detection using retinal images guided by disease causal estimation. This model leverages disease-specific features, integrating disease causal relationships and interactions between image features and disease conditions. Specifically, 1) the interactions between disease and image features are captured by cross-attention in a transformer decoder. 2) The causal relationships among diseases are automatically estimated as the directed acyclic graph (DAG) based on the dataset itself and are utilized to regularize disease-specific feature learning with disease causal interaction. 3) A novel retinal multi-disease dataset of 500 patients, including six lesion labels, was generated for evaluation purposes. Compared with other methods, the proposed approach not only achieves multi-disease diagnosis with high performance but also provides a method to estimate the causal relationships among diseases. We evaluated our method on two retinal datasets: a public colour fundus photography and an in-house fundus fluorescein angiography (FFA). The results show that the proposed method outperforms other state-of-the-art multi-label models. Our FFA database and code have been released (https://github.com/davelailai/multi-disease-detection-guided-by-causal-estimation.git).