arXiv:2606. 02602v1 Announce Type: new Abstract: In computational pathology, Whole Slide Images (WSIs) survival analysis is crucial for patient prognosis assessment, but it faces multiple technical challenges.
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.
arXiv:2607. 16260v1 Announce Type: cross Abstract: Multimodal survival analysis utilizing whole slide images (WSIs) and genomic profiles is fundamental for cancer prognosis.
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.
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.
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.
arXiv:2609.24894v1 Announce Type: cross Abstract: Whole-slide pathology images (WSIs) contain gigapixel-scale visual content, creating a major scalability challenge for slide-level multimodal large l...
arXiv:2606. 14828v1 Announce Type: cross Abstract: Leptomeningeal collaterals (LMCs) are an important prognostic factor in acute ischemic stroke.
arXiv:2609.00396v1 Announce Type: new Abstract: Histopathological whole slide images (WSIs) are central to cancer diagnosis, but their gigapixel scale, tissue heterogeneity, weak slide-level supervis...
arXiv:2510.14383v4 Announce Type: replace Abstract: Accurate brain tumor segmentation is significant for clinical diagnosis and treatment but remains challenging due to tumor heterogeneity. Mamba-bas...
arXiv:2607. 09788v1 Announce Type: cross Abstract: Alzheimer's disease (AD) is a common neurodegenerative disorder, and early diagnosis is of great significance for delaying disease progression and enabling timely intervention.
arXiv:2606. 14172v1 Announce Type: new Abstract: Multimodal Attributed Graphs (MAGs) model real-world entities by coupling graph topology with heterogeneous attributes such as text and images.