bibtext: @InProceedings{ Xie_Multidisease_MICCAI2024,
                   author = { Xie, Jianyang and Chen, Xiuju and Zhao, Yitian and Meng, Yanda and Zhao, He and Nguyen, Anh and Li, Xiaoxin and Zheng, Yalin },
                   title = { { Multi-disease Detection in Retinal Images Guided by Disease Causal Estimation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15001 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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 color 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 at https://github.com/davelailai/multi-disease-detection-guided-by-causal-estimation.git.
title: Multi-disease Detection in Retinal Images Guided by Disease Causal Estimation
authors: Xie, Jianyang Chen, Xiuju Zhao, Yitian Meng, Yanda Zhao, He Nguyen, Anh Li, Xiaoxin Zheng, Yalin