Multi-label ocular abnormalities detection with semantic dictionary learning

Anneke Annassia Putri Siswadi, Stéphanie Bricq, Fabrice Mériaudeau

Abstract: Early detection of ocular abnormalities is important in preventing retinal damage that can cause blindness. As there is a high possibility for a patient to suffer from more than one ocular abnormality, diagnosis of multiple abnormalities (multi-label detection) is essential considering its efficiency. Multi-label detection of ocular abnormalities is still challenging due to the presence of rare ocular abnormalities. Only a limited number of data on rare ocular abnormalities are available which usually makes these abnormalities be ignored in multi-label detection. The aim of this study is to detect multi-label ocular abnormalities from color fundus images for both frequent and rare cases. The proposed method addresses this challenge by combining the visual features extracted from a color fundus image and the label co-occurrence dependencies extracted from linguistic features. The label co-occurrence approach has not been used so far for multi-label detection in medical applications as it can significantly reduce detection accuracy because of uncorrelated image and label features. However, this study shows that the label co-occurrence approach can increase the performance of multi-label detection of ocular abnormalities by tackling the miss-representation of the correlation between image and label features using semantic dictionary learning, taking into consideration the presence of labels that belongs to out-of-vocabulary (OOV) words as it has irrelevant label features.