Title: Learning Asymmetric Phase Dynamics via Clinically-Guided Spatiotemporal Fusion
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Multiphase CECT, clinically guided AI, spatiotemporal fusion, radiological priors

Abstract: Hepatocellular carcinoma is a leading cause of cancer-related mortality, where accurate tumor characterization is crucial for guiding treatment. In clinical consensus, multiphase contrast-enhanced computed tomography (CECT) is indispensable: arterial (ART), portal venous (PV), and delayed (DL) phases jointly depict tumor vascularization, perfusion heterogeneity, and fibrotic evolution, forming the very basis of radiologists’ diagnostic reasoning.  **_Surprisingly, despite this well-established clinical value_**, most AI models still rely on ① single-phase inputs or naïve stacking of multiphase scans, ② ignoring temporal hemodynamics and lacking interpretability.
To bridge this gap, we present **Clinically-Guided Spatiotemporal Deep Fusion Network (CSF-Net)**, the first framework that explicitly embeds radiological knowledge into multiphase modeling. **CSF-Net** incorporates three synergistic components: the **multi-phase clinical-quantitative synergy branch (MCQS)** for phase-specific encoding, the **temporal-aware local feature refinement module (TLFR)** for perfusion dynamics, and the **query-interaction enhancement fusion module (QIEF)** for cross-phase alignment. By aligning AI modeling design with radiologists’ logic, **CSF-Net** establishes a clinically grounded interpretability paradigm, which inevitably yields superior performance gains. Extensive experiments on two CECT benchmarks, PLC-CECT and MPLL, demonstrate that **CSF-Net** achieves state-of-the-art performance. Our codes are available at: <https://anonymous.4open.science/r/ICLR26_CSF-Net-63E3/>.

TL;DR: We introduce CSF-Net, a clinically guided spatiotemporal fusion network for multiphase CECT that aligns AI with radiologists’ logic, naturally achieving state-of-the-art liver tumor analysis.