Multi-scale context-aware networks for quantitative assessment of colorectal liver metastases
Zhaoyang Xu, Qianni Zhang · 2018
Colorectal Liver Metastases is the main cause of death in patients with colorectal cancer. Accurate histopathological assessment of the tumour regression - pathological response - in surgically resected liver matastases after preoperative chemotherapy is of paramount importance for prognostic stratification and subsequent planning of treatment. In this paper, we propose two multi-scale deep neural networks to address the absence of contextual information when using the patch-based methods for segmentation tasks in histopathological image analysis. The proposed networks are capable of integrating the texture features from a high magnification level and the contextual information from a low magnification level to achieve more accurate results. Extensive experiments have been performed on a dataset of whole slide scans of resected colorectal liver metastases annotated by experts. Our results demonstrate that the proposed multi-scale networks outperform the networks trained on any single magnification level alone.