Abstract
As reported by the International Agency for Research on Cancer (IARC), the global incidence of cancer reached nearly 20 million new cases in recent years, with cancer-related fatalities amounting to around 9.7 million. This underscores the profound impact cancer has on public health worldwide. Deep learning has become a mainstream approach in cancer recognition. Despite its significant progress, deep learning is known for its requirement of large quantities of labeled data. Few-shot learning addresses this limitation by reducing the need for extensive labeled samples. In the field of cancer recognition, data collection is particularly challenging due to the scarcity of categories compared to other fields, and current few-shot learning methods have not yielded satisfactory results. To tackle this, we propose an auxiliary meta-learning strategy for cancer recognition. During the auxiliary training phase, the feature mapping model is trained in conjunction with external data. This process neutralizes the prediction probability of misclassification, allowing the model to more readily learn distinguishing features and avoid performance degradation caused by discrepancies in external data. Additionally, the redundancy of some input principal components in the feature mapping model is reduced, while the implicit information within these components is extracted. The training process is further accelerated by utilizing depthwise over-parameterized convolutional layers. Moreover, the implementation of a three-branch structure contributes to faster training and enhanced performance. In the meta-training stage, the feature mapping model is optimized within the embedding space, utilizing category prototypes and cosine distance. During the meta-testing phase, a small number of labeled samples are employed to classify unknown data. We have conducted extensive experiments on the BreakHis, Pap smear, and ISIC 2018 datasets. The results demonstrate that our method achieves superior accuracy in cancer recognition. Furthermore, experiments on few-shot benchmark datasets indicate that our approach exhibits excellent generalization capabilities.