Spatial Transcriptomics (ST) reveals the spatial distribution of gene expression in tissues, offering critical insights into biological processes and disease mechanisms. However, the high cost, limited coverage, and technical complexity of current ST technologies restrict their widespread use in clinical and research settings, making obtaining high-resolution transcriptomic profiles across large tissue areas challenging. Predicting ST from H&E-stained histology images has emerged as a promising alternative to address these limitations but remains challenging due to the heterogeneous relationship between histomorphology and gene expression, which is affected by substantial variability across patients and tissue sections. In response, we propose PH2ST, a prompt-guided hypergraph learning framework, which leverages limited ST signals to guide multi-scale histological representation learning for accurate and robust spatial gene expression prediction. Extensive evaluations on two public ST datasets and multiple prompt sampling strategies simulating real-world scenarios demonstrate that PH2ST not only outperforms existing state-of-the-art methods, but also shows strong potential for practical applications such as imputing missing spots, ST super-resolution, and local-to-global prediction, highlighting its value for scalable and cost-effective spatial gene expression mapping in biomedical contexts.

Interactive segmentation offers greater clinical potential in computational pathology compared to traditional automatic segmentation. By incorporating interactive input, it addresses the limitations of fully automatic segmentation models, which often fail to meet pathologists' requirements and rely heavily on large-scale, pixel-level annotated datasets. However, current interactive segmentation methods struggle to balance interaction cost and segmentation performance, and they fail to adapt effectively to slide-level segmentation, a task that is even more crucial in routine pathology analysis. In this study, we propose a Prototype-Guided Interactive Segmentation (ProGIS) framework for pathological image segmentation, designed to deliver precise segmentation results efficiently with minimal interaction signals. ProGIS identifies all same-type tissue connected components in a single interaction and supports multi-class segmentation without predefined categories during inference. Moreover, ProGIS can be easily adapted for slide-level interactive segmentation. Specifically, ProGIS consists of three modules: Prototype Initialization, Prototype Navigation, and Local Refinement. First, the Prototype Initialization module identifies categorical prototypes, which are then utilized in the Prototype Navigation module to identify all tissue connected components belonging to the same type. The local refinement module further refines the segmentation results using detailed correction signals to ensure the accuracy of challenging-to-distinguish regions. We evaluate our framework on two regions of interest level and two slide-level pathological segmentation datasets, achieving new state-of-the-art performance with fewer interactions than existing methods. Our code is available at https://github.com/JSGe-AI/ProGIS.

Modeling clinical dynamics requires capturing how patient states evolve across visits while also accounting for the heterogeneous clinical signals present within each encounter. Existing approaches often emphasize temporal progression or intra-visit structure in isolation, leading to incomplete representations of patient trajectories and limited generalizability in realworld settings. We propose a bi-perspective clinical dynamics framework that jointly models longitudinal evolution and finegrained visit-level structure as two complementary axes of patient state formation. This design produces a unified trajectory representation that preserves long-range dependencies while remaining sensitive to intra-visit diagnostic, procedural, and treatmentrelated cues. To further stabilize prediction under data sparsity and heterogeneous patient profiles, the framework incorporates an optional similarity-guided refinement module that leverages relational patterns across historical cases without increasing inference overhead. Evaluations on two large critical-care datasets demonstrate that the proposed approach consistently yields stronger predictive behavior, enhanced robustness under sparse and irregular trajectories, and substantially improved computational efficiency compared with widely adopted architectures. These results highlight the effectiveness of structuring clinical event prediction around complementary temporal and contextual perspectives, offering a scalable pathway toward next-generation clinical decision-support systems.

Whole Slide Images (WSIs) are the digital version of pathology slides, central to computational pathology. The WSI pyramid makes it can offer a wide range of diagnostic information, from global tissue structures to detailed cellular features. However, current multi-instance and graph representation learning methods struggle to create a unified framework that effectively captures both local spatial awareness and global WSI representation, limiting their performance in critical tasks such as tumor staging. To this end, we propose a Prototypeaware Heterogeneous Graph ATtention (PHGAT) network that enables each region within a WSI to perceive the representations of its diverse heterogeneous neighbors. This, in turn, guides the learning of WSI-level heterogeneous embedding through multilevel prototypes. Specifically, we introduce three node relations (i.e., local, non-local, and hierarchical) into WSI heterogeneous graph construction and design a novel Heterogeneous Calibration Graph ATtention (HC-GAT) module to propagate the various heterogeneous neighbor node representations within graphs. Then, a Level-aware Prototype Attention module is proposed to obtain prototypes from different levels by aggregating the node representations via a set of trainable query embeddings. Lastly, based on these learned prototypes, a prototype-aware hierarchical pooling module is designed to generate the final heterogeneous embedding of each WSI. Extensive experiments on six diverse datasets across three cancer types and two specific diagnostic tasks show that the proposed framework significantly outperforms the state-of-the-art tumor staging methods and performs comparably in cancer subtyping.

Deep learning has shown remarkable performance in integrating multimodal data for survival prediction. However, existing multimodal methods mainly focus on single cancer types and overlook the challenge of generalization across cancers. In this work, we are the first to reveal that multimodal prognosis models often generalize worse than unimodal ones in cross-cancer scenarios, despite the critical need for such robustness in clinical practice. To address this, we propose a new task: Cross-Cancer Single Domain Generalization for Multimodal Prognosis, which evaluates whether models trained on a single cancer type can generalize to unseen cancers. We identify two key challenges: degraded features from weaker modalities and ineffective multimodal integration. To tackle these, we introduce two plug-and-play modules: Sparse Dirac Information Rebalancer (SDIR) and Cancer-aware Distribution Entanglement (CADE). SDIR mitigates the dominance of strong features by applying Bernoulli-based sparsification and Dirac-inspired stabilization to enhance weaker modality signals. CADE, designed to synthesize the target domain distribution, fuses local morphological cues and global gene expression in latent space. Experiments on a four-cancer-type benchmark demonstrate superior generalization, laying the foundation for practical, robust cross-cancer multimodal prognosis. Code is available at here.

MOTIVATION: Survival analysis is a branch of statistics that is crucial in medicine for modeling the time to critical events such as death or relapse, in order to improve treatment strategies and patient outcomes. Selecting survival models often involves a trade-off between performance and interpretability; deep learning models offer high performance but lack the transparency of more traditional approaches. This poses a significant issue in medicine, where practitioners are reluctant to use black-box models for critical patient decisions. RESULTS: We introduce CoxKAN, a Cox proportional hazards Kolmogorov-Arnold Network for interpretable, high-performance survival analysis. Kolmogorov-Arnold Networks (KANs) were recently proposed as an interpretable and accurate alternative to multi-layer perceptrons. We evaluated CoxKAN on four synthetic and nine real datasets, including five cohorts with clinical data and four with genomics biomarkers. In synthetic experiments, CoxKAN accurately recovered interpretable hazard function formulae and excelled in automatic feature selection. Evaluations on real datasets showed that CoxKAN consistently outperformed the traditional Cox proportional hazards model (by up to 4% in C-index) and matched or surpassed the performance of deep learning-based models. Importantly, CoxKAN revealed complex interactions between predictor variables and uncovered symbolic formulae, which are key capabilities that other survival analysis methods lack, to provide clear insights into the impact of key biomarkers on patient risk. AVAILABILITY AND IMPLEMENTATION: CoxKAN is available at GitHub and Zenodo.