Joint model- and immunohistochemistry-driven few-shot learning scheme for breast cancer segmentation on 4D DCE-MRI

Automatic segmentation of breast cancer on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which reveals both temporal and spatial profiles of the foundational anatomy, plays a crucial role in the clinical diagnosis and treatment of breast cancer. Recently, deep learning has witnessed great advances in tumour segmentation tasks. However, most of those high-performing models require a large number of annotated gold-standard samples, which remains a challenge in the accurate segmentation of 4D DCE-MRI breast cancer with high heterogeneity. To address this problem, we propose a joint immunohistochemistry- (IHC) and model-driven few-shot learning scheme for 4D DCE-MRI breast cancer segmentation. Specifically, a unique bidirectional convolutional recurrent graph attention autoencoder (BiCRGADer) is developed to exploit the spatiotemporal pharmacokinetic characteristics contained in 4D DCE-MRI sequences. Moreover, the IHC-driven strategy that employs a few-shot learning scenario optimizes BiCRGADer by learning the features of MR imaging phenotypes of specific molecular subtypes during training. In particular, a parameter-free module (PFM) is designed to adaptively enrich query features with support features and masks. The combined model- and IHC-driven scheme boosts performance with only a small training sample size. We conduct methodological analyses and empirical evaluations on datasets from The Cancer Imaging Archive (TCIA) to justify the effectiveness and adaptability of our scheme. Extensive experiments show that the proposed scheme outperforms state-of-the-art segmentation models and provides a potential and powerful noninvasive approach for the artificial intelligence community dealing with oncological applications.