SeekFlow: Synergizing Radiology and Pathology Foundation Models for Precision Oncology via Knowledge-Guided Evidence Flow

Integrating macro-scale radiology and micro-scale pathologyprovides complementary insights crucial for precision oncology. How-ever, this integration is often hindered by modality discrepancy. The fea-ture embeddings from heterogeneous foundation models reside on poorlyaligned latent manifolds, and naive static fusion may therefore mix unre-lated semantic neighborhoods, leading to topological degeneracy and se-mantic entanglement. To bridge this gap, we propose a novel multimodallearning framework for Synthesizing radiology and pathology EvidencEvia Knowledge-guided FLOW matching. Specifically, we construct ashared feature manifold anchored by learnable clinical prototypes, en-abling the disentanglement and synthesis of shared and modality-specificfeature evidence. We then reformulate multimodal integration as a trans-port problem via knowledge-guided flow matching and seek continuousflows that smoothly transport features onto this semantic substrate. Thisparadigm preserves the intrinsic topology of the feature spaces and re-duces feature collapse. Furthermore, an uncertainty-aware evidential ag-gregator is incorporated to synthesize cross-modal feature evidence forreliable decision-making. Extensive experiments on three cancer datasets(i.e., TCGA Glioma, Gastric Cancer, and Chondrosarcoma) demonstratethe effectiveness of SeekFlow. Notably, SeekFlow consistently achievesstate-of-the-art performance across all benchmarks, outperforming ex-isting multimodal fusion methods in both diagnosis and prognosis tasks.The code is available at https://github.com/HKU-MedAI/SeekFlow.