Leveraging Cardiac Imaging to Improve ECG-Based Detection of Chagas Disease in Resource-Constrained Settings
该研究通过将心脏磁共振成像的知识转移至心电图，提高了资源受限地区查加斯病的检测准确性。
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Investigating Linear Probe Robustness to Linguistic Register, Medical Specialty, and Corpus Shifts in Medical QA
研究通过构建包含不同写作风格和医学专业的500条MedQA数据集，探索线性探针在医疗问答中面对语言风格、医学专业及语料库变化时的鲁棒性。
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Pretrained, Curriculum-Tuned, and Ensembled: A Tracer-Aware Interactive Segmentation Pipeline for AutoPET V
本文提出TRIAGE方法，通过预训练、课程调优和集成学习解决全身PET/CT中交互式病灶分割问题。
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When Names Cross Scripts: A Source-Grounded Benchmark for Historical Entity Reconciliation in the Mongol World
本文提出MHER基准，用于解决蒙古历史人物身份对齐问题，通过源证据而非仅名字匹配，显著提高准确性。
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leaspy: LEArning Spatiotemporal Patterns in PYthon
This work addresses the challenge of temporal misalignment in longitudinal data arising from inter-individual differences in the onset and progression rates of dynamic processes. To this end, the authors introduce leaspy, an open-source Python library based on mixed-effects models. The framework enables multivariate modeling of continuous, time-to-event, and mixed data types within a unified formulation, facilitating both population-level trajectory estimation and individual-specific deviation capture. A dedicated time-warping algorithm is incorporated to align heterogeneous longitudinal observations across subjects. Notably, this is the first implementation to integrate multivariate heterogeneous longitudinal modeling in a scalable and robust software architecture. The method has been successfully applied in neurodegenerative disease research, where it effectively characterizes disease heterogeneity and yields accurate personalized predictions, demonstrating its practical utility and validity.
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Leveraging Cardiac Imaging to Improve ECG-Based Detection of Chagas Disease in Resource-Constrained Settings
该研究通过将心脏磁共振成像的知识转移至心电图，提高了资源受限地区查加斯病的检测准确性。
0 citationsRead paper
Investigating Linear Probe Robustness to Linguistic Register, Medical Specialty, and Corpus Shifts in Medical QA
研究通过构建包含不同写作风格和医学专业的500条MedQA数据集，探索线性探针在医疗问答中面对语言风格、医学专业及语料库变化时的鲁棒性。
0 citationsRead paper
Pretrained, Curriculum-Tuned, and Ensembled: A Tracer-Aware Interactive Segmentation Pipeline for AutoPET V
本文提出TRIAGE方法，通过预训练、课程调优和集成学习解决全身PET/CT中交互式病灶分割问题。
0 citationsRead paper
When Names Cross Scripts: A Source-Grounded Benchmark for Historical Entity Reconciliation in the Mongol World
本文提出MHER基准，用于解决蒙古历史人物身份对齐问题，通过源证据而非仅名字匹配，显著提高准确性。
0 citationsRead paper
leaspy: LEArning Spatiotemporal Patterns in PYthon
This work addresses the challenge of temporal misalignment in longitudinal data arising from inter-individual differences in the onset and progression rates of dynamic processes. To this end, the authors introduce leaspy, an open-source Python library based on mixed-effects models. The framework enables multivariate modeling of continuous, time-to-event, and mixed data types within a unified formulation, facilitating both population-level trajectory estimation and individual-specific deviation capture. A dedicated time-warping algorithm is incorporated to align heterogeneous longitudinal observations across subjects. Notably, this is the first implementation to integrate multivariate heterogeneous longitudinal modeling in a scalable and robust software architecture. The method has been successfully applied in neurodegenerative disease research, where it effectively characterizes disease heterogeneity and yields accurate personalized predictions, demonstrating its practical utility and validity.
0 citationsRead paper