Pose2Muscle: Structured Spatio-Temporal Decoding for Discrete Muscle Activity Estimation from Human Pose
为解决长期监测肌肉活动不便的问题，提出Pose2Muscle框架，通过人体姿态估计离散肌肉活动状态，无需使用sEMG信号。
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A multicenter benchmark and clinically structured metric for coronary CTA report generation
研究建立了多中心基准和临床结构化指标CSM_CCTA，用于评估冠状动脉CTA报告自动生成的准确性与解剖特异性。
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Coronary Mask Guided Registration for Continuous Time 4D Cardiac CT Dataset Construction
本文提出冠状动脉掩模引导配准(CMGR)方法，旨在通过减少运动伪影和保持时间连续性来构建适用于作为伪真值的4D心脏CT数据集。
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CMCNet: Aligning Ultrasound Image Embeddings with Textual TI-RADS Representations for Fine-Grained Thyroid Classification
This study addresses the underutilization of feature-level supervision in multi-class thyroid nodule ultrasound classification by proposing CMCNet. The method employs text embeddings as stable proxy representations for risk stratification, achieving cross-modal alignment between ultrasound images and TI-RADS textual descriptions via a Center-Margin Contrastive loss to guide image-only fine-grained grading with structured features. Experimental results demonstrate that CMCNet significantly outperforms InfoNCE and multi-task baselines in data efficiency and robustness. Notably, it exhibits superior performance under class-imbalanced conditions, effectively enhancing the accuracy of nodule risk stratification.
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Pose2Muscle: Structured Spatio-Temporal Decoding for Discrete Muscle Activity Estimation from Human Pose
为解决长期监测肌肉活动不便的问题，提出Pose2Muscle框架，通过人体姿态估计离散肌肉活动状态，无需使用sEMG信号。
0 citationsRead paper
Multiple Myeloma Lesion Segmentation on Whole-Body Diffusion-Weighted Imaging via Efficient Anatomical Anticipation and Multimodal Confirmation
本文针对多发性骨髓瘤病灶在全身扩散加权成像上自动分割的难题，提出了一种结合高效解剖预测和多模态确认的两阶段框架。
0 citationsRead paper
A multicenter benchmark and clinically structured metric for coronary CTA report generation
研究建立了多中心基准和临床结构化指标CSM_CCTA，用于评估冠状动脉CTA报告自动生成的准确性与解剖特异性。
0 citationsRead paper
Coronary Mask Guided Registration for Continuous Time 4D Cardiac CT Dataset Construction
本文提出冠状动脉掩模引导配准(CMGR)方法，旨在通过减少运动伪影和保持时间连续性来构建适用于作为伪真值的4D心脏CT数据集。
0 citationsRead paper
CMCNet: Aligning Ultrasound Image Embeddings with Textual TI-RADS Representations for Fine-Grained Thyroid Classification
This study addresses the underutilization of feature-level supervision in multi-class thyroid nodule ultrasound classification by proposing CMCNet. The method employs text embeddings as stable proxy representations for risk stratification, achieving cross-modal alignment between ultrasound images and TI-RADS textual descriptions via a Center-Margin Contrastive loss to guide image-only fine-grained grading with structured features. Experimental results demonstrate that CMCNet significantly outperforms InfoNCE and multi-task baselines in data efficiency and robustness. Notably, it exhibits superior performance under class-imbalanced conditions, effectively enhancing the accuracy of nodule risk stratification.
0 citationsRead paper