arXiv:2606. 19140v1 Announce Type: new Abstract: Accurate survival prediction is essential for personalized treatment planning in head and neck cancer, yet remains challenging due to the heterogeneous and high-dimensional nature of multimodal clinical data.
By Hugo Miccinilli, Theo Di Piazza
arXiv:2607. 16260v1 Announce Type: cross Abstract: Multimodal survival analysis utilizing whole slide images (WSIs) and genomic profiles is fundamental for cancer prognosis.
By Jialong Zhong, Tingwei Liu, Baokun Yue, Jingjing Li, Yongri Piao, Miao Zhang, Leiye Liu, Jiahong Jiang, Wei Ji, Huchuan Lu
The paper introduces the Semantic-Aware Subgraph State Space Model (SASG-SSM) for classifying whole slide images in histopathology. It groups spatially connected patches into semantic subgraphs that preserve internal spatial organization, then uses a graph neural network encoder combined with a Mamba-based state space encoder to integrate local and global contextual information. Experiments on four WSI subtyping datasets show consistent performance gains over state‑of‑the‑art methods, with additional robustness in small‑cohort and few‑shot scenarios.
The paper introduces GDMRG, a Graph-Augmented Dual-Stream Medical Report Generation framework that incorporates a Topological Knowledge Internalization module using a Graph Convolutional Network to encode disease co-occurrence priors. It employs a dual-stream classifier—one branch generating diagnostic prompts under topological constraints and an auxiliary branch dynamically calibrating decision boundaries for imbalanced samples—alongside a Diagnosis-Guided Spatial Attention mechanism to align visual features with clinical semantics. Experiments on MIMIC-CXR show competitive clinical efficacy and natural language fluency, with strong zero-shot performance on IU X-Ray.
By Moyu Tang, Shangkun Sima, Chupei Tang, Junxiao Kong, Di Wang, Tianchi Lu
The paper introduces the Semantic-Aware Subgraph State Space Model (SASG-SSM) for classifying whole slide images (WSIs) in histopathology. It groups spatially connected patches into semantic-aware subgraphs that preserve internal spatial organization, then uses a graph neural network encoder combined with a Mamba-based state space encoder to capture both local and global contextual information. Experiments on four WSI subtyping datasets show consistent performance gains over state‑of‑the‑art methods, with additional robustness in small‑cohort and few‑shot scenarios.
By Feixing Chen, Hao Lu, Lin Luo, Yan Xu
The paper introduces an end‑to‑end framework for detecting and classifying cells in pathology images by jointly modeling visual features and instance‑level interactions. It employs a dynamic graph construction module that builds cell graphs from learnable queries and an instance‑aware graph network that filters and reorganizes features, integrating appearance and relational evidence. Experiments on multiple staining protocols show the method surpasses existing approaches in both detection and classification accuracy.
By Ruochen Liu, Yalin Zheng, Jingxin Liu, Jianfeng Zhang, Shoujun Huang, Dexing Kong, Haofeng Li, Wei Lou