PULSE统一心脏影像分割、诊断与报告生成，支持跨模态少样本适配。
PULSE: A Unified Multi-Task Architecture for Cardiac Segmentation, Diagnosis, and Few-Shot Cross-Modality Clinical Adaptation
- 基于自监督表示与多任务联合优化，统一处理心脏影像多种任务。
- 在4个数据集上实现93.6%的分割准确率，跨模态泛化性能显著提升。
- 适合医疗影像研究者与临床智能系统开发者使用。
心脏影像分析长期分散于不同任务：解剖分割、疾病分类和临床报告生成通常由独立网络处理，训练数据分布各异。现有框架无法在一个架构中统一这些目标，且缺乏跨成像模态和数据集的泛化能力。我们提出PULSE，一种基于自监督表征的多任务视觉-语言框架，通过复合监督策略平衡区域重叠学习、像素级分类精度和边界感知的IoU优化。多尺度令牌重建解码器实现解剖分割，共享全局表征支持疾病分类与临床相关文本输出，使模型在单一架构内完成从像素到结构再到临床推理的跃迁。与以往任务专用流程不同，PULSE学习任务无关的心脏先验知识，在4个数据集上展现强泛化能力，并可通过极少标注数据适应新成像模态，推动向可扩展的心脏分析基础模型迈进。
原文摘要 · Abstract (English)
Cardiac image analysis remains fragmented across tasks: anatomical segmentation, disease classification, and grounded clinical report generation are typically handled by separate networks trained under different data regimes. No existing framework unifies these objectives within a single architecture while retaining generalization across imaging modalities and datasets. We introduce PULSE, a multi-task vision-language framework built on self-supervised representations and optimized through a composite supervision strategy that balances region overlap learning, pixel wise classification fidelity, and boundary aware IoU refinement. A multi-scale token reconstruction decoder enables anatomical segmentation, while shared global representations support disease classification and clinically grounded text output allowing the model to transition from pixels to structures and finally clinical reasoning within one architecture. Unlike prior task-specific pipelines, PULSE learns task-invariant cardiac priors, generalizes robustly across datasets, and can be adapted to new imaging modalities with minimal supervision. This moves the field closer to a scalable, foundation style cardiac analysis framework.
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