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.

Effective lesion detection in medical image is not only rely on the features of lesion region, but also deeply relative to the surrounding information. However, most current methods have not fully utilize it. What’s more, multi-scale feature fusion mechanism of most traditional detectors are unable to transmit detail information without loss, which makes it hard to detect small and boundary-ambiguous lesion in early stage disease. To address the above issues, we propose a novel intra- and across-layer feature interaction FCOS model (IAFI-FCOS) with a multi-scale feature fusion mechanism ICAF-FPN, which is a network structure with intra-layer context augmentation (ICA) block and across-layer feature weighting (AFW) block. Therefore, the traditional FCOS detector is optimized by enriching the feature representation from two perspectives. Specifically, the ICA block utilizes dilated attention to augment the context information in order to capture long-range dependencies between the lesion region and the surrounding. The AFW block utilizes dual-axis attention mechanism and weighting operation to obtain the efficient across-layer interaction features, enhancing the representation of detailed features. Our approach has been extensively experimented on both the private pancreatic lesion dataset and the public DeepLesion dataset, with AP50of 62.2% and 60.0%, respectively, and these results are 6.4% and 2.3% higher than the FCOS. Additionally, our model achieves SOTA results on the pancreatic lesion dataset.

Network regression models, where the outcome comprises the valued edge in a network and the predictors are actor or dyad-level covariates, are used extensively in the social and biological sciences. Valid inference relies on accurately modeling the residual dependencies among the relations. Frequently homogeneity assumptions are placed on the errors which are commonly incorrect and ignore critical natural clustering of the actors. In this work, we present a novel regression modeling framework that models the errors as resulting from a community-based dependence structure and exploits the subsequent exchangeability properties of the error distribution to obtain parsimonious standard errors for regression parameters.