In the field of computer-aided diagnosis, particularly for tumor diseases, segmentation is a prerequisite and primary step. Multi-modality images become essential for achieving accurate segmentation, which offer critical insights beyond the limitations of single-modality data. However, different modalities and images may suffer from different types of data imperfection, such as intensity non-uniformity, motion artifact, and low quality due to hardware limitations, which challenge image segmentation algorithms. To address this challenge, we propose a Reliable Evidential Discounting Network (REDNet), which is composed of three main modules: 1) the Intra-modality Consistency Evaluation Module (ICEM) measuring the data cohesion within the same modality; 2) the Cross-modality Difference Aggregation Module (CDAM) identifing data discrepancy across modalities; 3) the Discounting Fusion Module (DFM) processing the multi-modality evidence by applying discounting strategies to fuse the data. This approach maintains segmentation accuracy by effectively integrating multi-modality evidence, while discounting the influence of lower-quality data, ensuring reliable results despite the presence of image imperfections. We evaluated REDNet on two distinct datasets, BRATS2021 and an in-house pancreas dataset from Changhai Hospital. REDNet outperforms other methods, particularly in scenarios with imperfect image sources, and achieves reliable results in multi-modality tumor segmentation.

Clinical disease classification naturally follows a hierarchical reasoning process, where clinicians first distinguish coarse diagnostic categories (e.g., benign vs. malignant) before identifying specific subtypes to enhance diagnostic accuracy. While multi-view imaging offers complementary perspectives, most existing machine learning approaches perform flat decision fusion, ignoring this intrinsic hierarchy and handling inter-view conflicts and uncertainty primarily at the decision-fusion level. To address this gap, we propose the Hierarchical Multi-view Evidence Aggregation Network (H-MEAN), an end-to-end architecture that explicitly mimics clinical decision-making process. H-MEAN consists of three key modules: (i) Multi-View Fusion, which jointly encodes heterogeneous imaging views to capture complementary features; (ii) Hierarchical Tree Alignment, which structurally propagates diagnostic cues across granularity levels to enforce clinical consistency; and (iii) Hierarchical Evidential Aggregation, which leverages evidential deep learning to quantify uncertainty throughout the diagnostic hierarchy. Extensive experiments on two multi-view medical datasets demonstrate that H-MEAN consistently outperforms competitive methods, achieving higher classification accuracy, particularly in challenging settings with missing or noisy views.

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.

Real-world classification tasks often show an extremely imbalanced problem. The extreme imbalance will cause a strong bias that the decision boundary of the classifier is completely dominated by the categories with abundant samples, which are also called the head categories. Current methods have alleviated the imbalanced impact from mainly three aspects: class re-balance, decoupling and domain adaptation. However, the existing criterion with the winner-take-all strategy still leads to the crowding problem in the eigenspace. The head categories with many samples can extract features more accurately, but occupy most of the eigenspace. The tail categories sharing the rest of the narrow eigenspace are too crowded together to accurately extract features. Above these issues, we propose a novel T-distributed spherical metric for equalized eigenspace in the imbalanced classification, which has the following innovations: 1) We design the T-distributed spherical metric, which has the characteristics of high kurtosis. Instead of the winner-take-all strategy, the T-distributed spherical metric produces a high logit only when the extracted feature is close enough to the category center, without a strong bias against other categories. 2) The T-distributed spherical metric is integrated into the classifier, which is able to equalize the eigenspace for alleviating the crowding issue in the imbalanced problem. The equalized eigenspace by the T-distributed spherical classifier is capable of improving the accuracy of the tail categories while maintaining the accuracy of the head, which significantly promotes the intraclass compactness and interclass separability of features. Extensive experiments on large-scale imbalanced datasets verify our method, which shows superior results in the long-tailed CIFAR-100/-10 with the imbalanced ratio IR = 100/50. Our method also achieves excellent results on the large-scale ImageNet-LT dataset and the iNaturalist dataset with various backbones. In addition, we provide a case study of the real clinical classification of pancreatic tumor subtypes with 6 categories. Among them, the largest number of PDAC accounts for 315 cases, and the least CP has only 8 cases. After 4-fold cross-validation, we achieved a top-1 accuracy of 69.04%.