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MTCSNN: Multi-task Clinical Siamese Neural Network for Diabetic Retinopathy Severity Prediction by Feng, Chao; Hung, Jui Po; Li, Aishan; Yang, Jieping; Zhang, Xinyu is a scholarly article available to read on EtoBox.

What is MTCSNN: Multi-task Clinical Siamese Neural Network for Diabetic Retinopathy Severity Prediction about?

Diabetic Retinopathy (DR) has become one of the leading causes of vision impairment in working-aged people and is a severe problem worldwide. However, most of the works ignored the ordinal information of labels. In this project, we propose a novel design MTCSNN, a Multi-task Clinical Siamese Neural Network for Diabetic Retinopathy severity prediction task. The novelty of this project is to utilize the ordinal information among labels and add a new regression task, which can help the model learn more discriminative feature embedding for fine-grained classification tasks. We perform comprehensive experiments over the RetinaMNIST, comparing MTCSNN with other models like ResNet-18, 34, 50. Our results indicate that MTCSNN outperforms the benchmark models in terms of AUC and accuracy on the test dataset.

Author

Feng, Chao; Hung, Jui Po; Li, Aishan; Yang, Jieping; Zhang, Xinyu

Published

2022

Language

EN