The prediction of nanoparticles (NPs) distribution is crucial for the diagnosis and treatment of tumors. Recent studies indicate that the heterogeneity of tumor microenvironment (TME) highly affects the distribution of NPs across tumors. Hence, it has become a research hotspot to generate the NPs distribution by the aid of multi-modal TME components. However, the distribution divergence among multi-modal TME components may cause side effects i.e., the best unimodal model may outperform the joint generative model. To address the above issues, we propose a Divergence-Aware Multi-Modal Diffusion model (i.e., DAMM-Diffusion) to adaptively generate the prediction results from uni-modal and multi-modal branches in a unified network. In detail, the uni-modal branch is composed of the U-Net architecture while the multi-modal branch extends it by introducing two novel fusion modules i.e., Multi-Modal Fusion Module (MMFM) and Uncertainty-Aware Fusion Module (UAFM). Specifically, the MMFM is proposed to fuse features from multiple modalities, while the UAFM module is introduced to learn the uncertainty map for cross-attention computation. Following the individual prediction results from each branch, the Divergence-Aware Multi-Modal Predictor (DAMMP) module is proposed to assess the consistency of multi-modal data with the uncertainty map, which determines whether the final prediction results come from multi-modal or uni-modal predictions. We predict the NPs distribution given the TME components of tumor vessels and cell nuclei, and the experimental results show that DAMM-Diffusion can generate the distribution of NPs with higher accuracy than the comparing methods. Additional results on the multi-modal brain image synthesis task further validate the effectiveness of the proposed method. The code is released†.

Graph anomaly detection (GAD) has become an increasingly important task across various domains. With the rapid development of graph neural networks (GNNs), GAD methods have achieved significant performance improvements. However, fairness considerations in GAD remain largely underexplored. Indeed, GNN-based GAD models can inherit and amplify biases present in training data, potentially leading to unfair outcomes. While existing efforts have focused on developing fair GNNs, most approaches target node classification tasks, where models often rely on simple layer architectures rather than autoencoder-based structures, which are the most widely used architecturs for anomaly detection. To address fairness in autoencoder-based GAD models, we propose DisEntangled Counterfactual Adversarial Fair (DECAF)-GAD, a framework that alleviates bias while preserving GAD performance. Specifically, we introduce a structural causal model (SCM) to disentangle sensitive attributes from learned representations. Based on this causal framework, we formulate a specialized autoencoder architecture along with a fairness-guided loss function. Through extensive experiments on both synthetic and real-world datasets, we demonstrate that DECAF-GAD not only achieves competitive anomaly detection performance but also significantly enhances fairness metrics compared to baseline GAD methods. Our code is available at https://github.com/Tlhey/decaf_code.