  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.
