Pancreatic Cystic Neoplasms Lesion Detection for Non-contrast CT Image via Teacher-student Model

Abstract

Due to the low contrast between lesion features and surrounding tissues in non-contrast CT images, traditional detection methods often struggle to accurately identify and differentiate various types of cystic tumors. This limitation increases the risk of misdiagnosis and missed detection, thereby hindering the clinical application of contrast-free techniques. To address this issue, we propose a detection framework that combines the teacher-student model with feature interaction as a non-contrast pancreatic cystic tumor detection technique. The pseudo-labeling of contrast-free CT is detected using the teacher network, which serves as supervisory information for the student network to guide learning. The student network transfers lesion feature information from contrast-enhanced CT to enrich the representation of lesion features. To avoid the confusion of features in multi-phase CT, a multi-domain discriminator is introduced for adversarial learning to extract modality-independent features, which significantly improves the robustness of the model. The experimental results show that the proposed method outperforms the traditional method on plain CT, can effectively detect SCN and MCN lesions, and provides a reliable contrast-free diagnostic solution for the clinic.