Predictive Multi-Landmark OCT Tracking for Increased Motion Robustness
本文提出了一种预测性多标志点OCT跟踪方法，通过在多个跟踪标志点之间传播位置更新来提高高速运动下的跟踪鲁棒性。
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NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting
为解决患者数据复杂动态和随机性问题，提出NOAH模型，通过时间感知、多模态处理及生成式方法预测患者状态。
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EXPOSE: Explainable and Domain-Robust Embeddings from Pathology Vision Foundation Models using Sparse Autoencoders
为解决病理视觉基础模型(VFMs)对域变化敏感的问题，提出使用稀疏自编码器(SAEs)框架EXPOSE，通过识别并抑制域特定成分来提高跨域性能和嵌入鲁棒性。
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Layer Selection in Feature-Based Losses Affects Image Quality and Microstructural Consistency in Deep Learning Super-Resolution of Brain Diffusion MRI
This study addresses the challenges of high-resolution diffusion MRI, which is constrained by hardware limitations and prolonged scan times. Existing deep learning–based super-resolution methods often introduce artifacts and compromise microstructural consistency. Leveraging 7T human connectome data, the authors employ a UNet architecture for 2D super-resolution reconstruction and systematically evaluate the impact of feature loss derived from different layers of VGG16 on image fidelity and diffusion signal consistency. They find, for the first time, that deeper-layer feature losses induce grid-like artifacts and bias diffusion parameter estimation, whereas the shallowest-layer feature loss best preserves microstructural integrity. Experiments demonstrate that this strategy effectively suppresses artifacts even at up to 9× super-resolution, yielding reconstructions highly consistent with ground-truth high-resolution data, with both image signal-to-noise ratio and VGG layer depth jointly modulating artifact manifestation.
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Reinforcement Learning for Tool-Calling Agents in Fast Healthcare Interoperability Resources (FHIR)
This work addresses the challenge that existing agents struggle to perform accurate multi-hop clinical reasoning under the FHIR standard, often failing due to incorrect resource selection or violations of graph traversal constraints. To overcome this, the study introduces reinforcement learning into FHIR-based tool-calling agents for the first time, proposing an end-to-end post-training framework that formulates multi-step reasoning as a sequential decision-making problem over a structured knowledge graph. Integrating the CodeAct agent architecture with an LLM-based Judge reward mechanism grounded in execution outcomes, the approach achieves a significant improvement on FHIR-AgentBench: using the Qwen3-8B model, it raises answer accuracy from 50% (o4-mini) to 77%, substantially outperforming closed-source baselines while strictly adhering to healthcare data integrity constraints.
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Predictive Multi-Landmark OCT Tracking for Increased Motion Robustness
本文提出了一种预测性多标志点OCT跟踪方法，通过在多个跟踪标志点之间传播位置更新来提高高速运动下的跟踪鲁棒性。
0 citationsRead paper
NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting
为解决患者数据复杂动态和随机性问题，提出NOAH模型，通过时间感知、多模态处理及生成式方法预测患者状态。
0 citationsRead paper
EXPOSE: Explainable and Domain-Robust Embeddings from Pathology Vision Foundation Models using Sparse Autoencoders
为解决病理视觉基础模型(VFMs)对域变化敏感的问题，提出使用稀疏自编码器(SAEs)框架EXPOSE，通过识别并抑制域特定成分来提高跨域性能和嵌入鲁棒性。
0 citationsRead paper
Layer Selection in Feature-Based Losses Affects Image Quality and Microstructural Consistency in Deep Learning Super-Resolution of Brain Diffusion MRI
This study addresses the challenges of high-resolution diffusion MRI, which is constrained by hardware limitations and prolonged scan times. Existing deep learning–based super-resolution methods often introduce artifacts and compromise microstructural consistency. Leveraging 7T human connectome data, the authors employ a UNet architecture for 2D super-resolution reconstruction and systematically evaluate the impact of feature loss derived from different layers of VGG16 on image fidelity and diffusion signal consistency. They find, for the first time, that deeper-layer feature losses induce grid-like artifacts and bias diffusion parameter estimation, whereas the shallowest-layer feature loss best preserves microstructural integrity. Experiments demonstrate that this strategy effectively suppresses artifacts even at up to 9× super-resolution, yielding reconstructions highly consistent with ground-truth high-resolution data, with both image signal-to-noise ratio and VGG layer depth jointly modulating artifact manifestation.
0 citationsRead paper
Reinforcement Learning for Tool-Calling Agents in Fast Healthcare Interoperability Resources (FHIR)
This work addresses the challenge that existing agents struggle to perform accurate multi-hop clinical reasoning under the FHIR standard, often failing due to incorrect resource selection or violations of graph traversal constraints. To overcome this, the study introduces reinforcement learning into FHIR-based tool-calling agents for the first time, proposing an end-to-end post-training framework that formulates multi-step reasoning as a sequential decision-making problem over a structured knowledge graph. Integrating the CodeAct agent architecture with an LLM-based Judge reward mechanism grounded in execution outcomes, the approach achieves a significant improvement on FHIR-AgentBench: using the Qwen3-8B model, it raises answer accuracy from 50% (o4-mini) to 77%, substantially outperforming closed-source baselines while strictly adhering to healthcare data integrity constraints.
0 citationsRead paper