Refocal Loss in Transformer for Long-Tailed Multi-Granularity Cataract Classification

Different cataract types and various severities usually require different countermeasures. For automatic cataract diagnosis, existing cataract classification methods group cataracts into common types, such as nuclear cataract, cortical cataract, and posterior subcapsular cataract, while existing cataract grading works aim to achieve fine-grained evaluation of the severity of the most common types of cataract. The severity assessment differs among various types of cataracts. Existing work is limited in predicting various cataract types at different granularity levels. In order to improve diagnostic efficiency, our study explores this matter in the context of multi-granularity cataract classification. Firstly, a large-scale dataset called Multi-Granularity Long-Tailed Cataract is collected. Secondly, an end-to-end training network is proposed, in which the Transformer is investigated for the extraction of multi-granularity cataract features. What is more, considering the imbalanced cataract data with the long-tailed distribution, the Refocal loss is proposed to rebalance the loss contribution of different classes by enhancing the reciprocal value of the effective number of samples. Compared with state-of-the-art methods, the experiments conducted on the multi-granularity cataract classification dataset demonstrate that the proposed model achieves the highest Precision of 78.22%, F1-score of 68.35%, Kappa of 64.38% and MCC of 64.49%, indicating that the proposed framework is promising in offering physicians reliable quantitative evaluations for multi-granularity cataract classification, which can help guide appropriate treatment decisions before the patient's cataracts worsen.