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

arXiv: https://arxiv.org/abs/2503.00052 · HTML: https://mlsanity.com/paper/2503.00052/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 3 of 3)

- [Evaluation of Output Embeddings for Fine-Grained Image Classification](https://mlsanity.com/paper/1409.8403/) (2014)
- [Link the head to the "beak": Zero Shot Learning from Noisy Text Description at Part Precision](https://mlsanity.com/paper/1709.01148/) (2017)
- [Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review](https://mlsanity.com/paper/2004.14143/) (2020)

## Abstract (arXiv)

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.