RURA-Net: A general disease diagnosis method based on Zero-Shot Learning

• URL: http://arxiv.org/abs/2503.00052v1
• Date: Wed, 26 Feb 2025 16:41:32 GMT
• Title: RURA-Net: A general disease diagnosis method based on Zero-Shot Learning
• Authors: Yan Su, Qiulin Wu, Weizhen Li, Chengchang Pan, Honggang Qi,
• Abstract summary: Our study proposes a general disease diagnosis approach based on Zero-Shot Learning.<n>Siamese neural network is used to find similar diseases for the target diseases.<n>U-Net segmentation model is used to accurately segment the key lesions of the disease.
• Score: 6.528066461340262
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The training of deep learning models relies on a large amount of labeled data. However, the high cost of medical labeling seriously hinders the development of deep learning in the medical field. Our study proposes a general disease diagnosis approach based on Zero-Shot Learning. The Siamese neural network is used to find similar diseases for the target diseases, and the U-Net segmentation model is used to accurately segment the key lesions of the disease. Finally, based on the ResNet-Agglomerative clustering algorithm, a clustering model is trained on a large number of sample data of similar diseases to obtain a approximate diagnosis of the target disease. Zero-Shot Learning of the target disease is then successfully achieved. To evaluate the validity of the model, we validated our method on a dataset of ophthalmic diseases in CFP modality. The external dataset was used to test its performance, and the accuracy=0.8395, precision=0.8094, recall=0.8463, F1 Score=0.8274, AUC=0.9226, which exceeded the indexes of most Few-Shot Learning and One-Shot Learning models. It proves that our method has great potential and reference value in the medical field, where annotation data is usually scarce and expensive to obtain.

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