Addressing Glaucoma Structure-Function Relationship: A Multi-Task Learning Framework With Multi-Modal and Unpaired Data

Glaucoma, an irreversible neurodegenerative disorder, can lead to vision loss and blindness. Visual field (VF) tests are crucial for quantifying functional damage in glaucoma, but the tests are time-consuming and the results have high variations, influenced by subjective behaviors and psychological states of patients. Consequently, predicting VF test results using objective, non-invasive, reproducible optical coherence tomography (OCT) data coupled with deep learning modeling is promising in improving clinical care. However, existing methods only focus on predicting single VF indicators, such as threshold sensitivities or deviation maps, and have poor performance in severe glaucoma. Since different VF indicators are correlated, developing a joint prediction model is beneficial. Furthermore, the availability of more VF test data than corresponding OCTs in most datasets poses a challenge in utilizing unpaired VF test data. This study proposes a multi-modal, multi-task learning framework based on OCT data for VF prediction. We introduce a dynamic weighted loss function to improve prediction performance for eyes with severe glaucoma. Additionally, we construct a novel PairMatcher model for augmenting unpaired VF data. Extensive experiments demonstrate that our framework outperforms existing methods, showcasing its potential for VF prediction in glaucoma.