RURA-Net: A general disease diagnosis method based on Zero-Shot Learning
This addresses the challenge of expensive medical data annotation for disease diagnosis, though it appears incremental as it builds on existing zero-shot learning and segmentation techniques.
The paper tackles the problem of medical disease diagnosis with limited labeled data by proposing a zero-shot learning approach that combines Siamese networks, U-Net segmentation, and ResNet-Agglomerative clustering. The method achieved an accuracy of 0.8395 and AUC of 0.9226 on an ophthalmic disease dataset, outperforming few-shot and one-shot learning models.
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.