Semi-supervised Lymph Node Metastasis Classification with Pathology-Guided Label Sharpening and Two-Streamed Multi-scale Fusion

Haoshen Li, Yirui Wang, Jie Zhu, Dazhou Guo, Qinji Yu, Ke Yan, Le Lu, Xianghua Ye, Li Zhang, Qifeng Wang, Dakai Jin

Abstract: Diagnosis of lymph node (LN) metastasis in computed tomography (CT) scans is an essential yet challenging task for esophageal cancer staging and treatment planning. Deep learning methods can potentially address this issue by learning from large-scale, accurately labeled data. However, even for highly experienced physicians, only a portion of LN metastases can be accurately determined in CT. Previous work conducted supervised training with a relatively small number of annotated LNs and achieved limited performance. In our work, we leverage the teacher-student semi-supervised paradigm and explore the potential of using a large amount of unlabeled LNs in performance improvement. For unlabeled LNs, pathology reports can indicate the presence of LN metastases within the lymph node station (LN-station). Hence, we propose a pathology-guided label sharpening loss by combining the metastasis status of LN-station from pathology reports with predictions of the teacher model. This combination assigns pseudo labels for LNs with high confidence and then the student model is updated for better performance. Besides, to improve the initial performance of the teacher model, we propose a two-stream multi-scale feature fusion deep network that effectively fuses the local and global LN characteristics to learn from labeled LNs. Extensive four-fold cross-validation is conducted on a patient cohort of 1052 esophageal cancer patients with corresponding pathology reports and 9961 LNs (3635 labeled and 6326 unlabeled). The results demonstrate that our proposed method markedly outperforms previous state-of-the-art methods by \(2.95\%\) (from \(90.23\%\) to \(93.18\%\)) in terms of the area under the receiver operating characteristic curve (AUROC) metric on this challenging task.