Combined model-driven and dual-cycle interactive strategy few-shot learning scheme for predicting breast cancer...

Deep learning has achieved significant success in solving classification problems in the medical field, which has, in turn, facilitated the development of continuous and real-time health monitoring systems. Yet medical images have extremely low data regimes, unlike natural images with a large amount of data. Hence, to classify medical images with a few per-class data samples is a challenging problem. In this paper, we propose a novel few-shot learning scheme, which jointly embeds a model-driven mechanism and dual-cycle interactive strategy, to predict breast cancer molecular subtypes based on a few DCE-MRI samples.

We present a unique spatio-temporal recurrent network classifier (STRNC) that predicts breast cancer molecular subtypes by learning spatial correlations and temporal dependencies in DCE-MRI. Moreover, the proposed dual-cycle interactive strategy (DCIS) formulates N-way C-shot tasks by applying task-specific adaptation to the support set and conducting meta-level evaluation on the query set, thus improving the model's generalization to unseen tasks.

Extensive experiments on public datasets have demonstrated that our proposed scheme achieves the best results, with a classification accuracy of 99.13 ±0.21 (%) for subtypes, and can accurately differentiate between individual molecular subtypes.

Overall, our proposed method is able to learn spatial correlation and temporal dependence in DCE-MRI and has the potential to guide clinical typing prediction.