Trusted Multi-view Pancreatic Tumor Subtypes Diagnosis through Augmented Opinion

In recent years, multi-view learning has been widely applied in medical imaging. However some low-quality data obtained may contain noise and artifacts, leading to unreliable decisions. Several trustworthy multi-view learning methods have been proposed to assess the uncertainty of decisions. In the diagnosis of pancreatic tumor subtypes (PTS), the subtle differences between subtypes make them difficult to distinguish. Without the guidance of clinical prior knowledge, these method often struggle to capture the key features specific to each subtype. To address these challenges, we propose a novel trustworthy multi-view learning method. By analyzing the potential malignancy risks of PTS, we classify all subtypes into three broad categories. This coarse-grained vague classification informed by clinical prior knowledge is introduced into the neural network as auxiliary information to generate augmented opinions. Additionally, to compensate for the lack of global features, we introduce a global view to supplement the feature space and strengthen multi-view learning. Finally, using an uncertainty-based combination rule, the augmented opinions extracted from multiple views are reliably combined for the final diagnosis. We conduct a series of experiments comparing our approach with the state-of-the-art multi-view learning methods. The experimental results validate the effectiveness of our proposed method, demonstrating its superior performance and trustworthiness.