Seeing Beyond the Lesion: Disease Recognition from Reactive CNS Tissue
研究解决了脑活检中因采样误差导致疾病未被诊断的问题，通过比较四种病理基础模型在多实例学习框架下的性能，发现即使在非病变组织中也能识别出疾病信号。
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QuantumBoostNet: A Hybrid Classical-Quantum Architecture for Enhanced Accuracy in Cardiac Ultrasound View Identification
本文提出QuantumBoostNet，一种结合经典与量子架构的方法，用于提高心脏超声视图识别准确性，通过两阶段训练及自适应头切换机制解决了医学图像中高噪声问题。
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Foundation Models Adaptation for Multi-View Multi-modal Cardiac MRI Segmentation and Direct Ejection Fraction Estimation
This work addresses the challenge of segmenting heterogeneous multi-center, multi-sequence, and multi-view cardiac MRI images and directly estimating left ventricular ejection fraction (LVEF). The authors propose a novel approach that integrates fine-tuned and frozen foundation models for cardiac MRI. Specifically, the CineMA model is fine-tuned to achieve high-accuracy segmentation of both cine and late gadolinium enhancement (LGE) images, while frozen models extract embedding features that, combined with an attention mechanism, enable multi-instance learning for end-to-end LVEF regression. This method represents the first effective integration of multiple foundation models, overcoming the limitations of single-model approaches. Experimental results demonstrate Dice scores of 0.862–0.902 for cine and 0.621–0.846 for LGE segmentation, with an LVEF estimation mean absolute error of 4.96% and a Pearson correlation coefficient of 0.91.
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Seeing Beyond the Lesion: Disease Recognition from Reactive CNS Tissue
研究解决了脑活检中因采样误差导致疾病未被诊断的问题，通过比较四种病理基础模型在多实例学习框架下的性能，发现即使在非病变组织中也能识别出疾病信号。
0 citationsRead paper
QuantumBoostNet: A Hybrid Classical-Quantum Architecture for Enhanced Accuracy in Cardiac Ultrasound View Identification
本文提出QuantumBoostNet，一种结合经典与量子架构的方法，用于提高心脏超声视图识别准确性，通过两阶段训练及自适应头切换机制解决了医学图像中高噪声问题。
0 citationsRead paper
Foundation Models Adaptation for Multi-View Multi-modal Cardiac MRI Segmentation and Direct Ejection Fraction Estimation
This work addresses the challenge of segmenting heterogeneous multi-center, multi-sequence, and multi-view cardiac MRI images and directly estimating left ventricular ejection fraction (LVEF). The authors propose a novel approach that integrates fine-tuned and frozen foundation models for cardiac MRI. Specifically, the CineMA model is fine-tuned to achieve high-accuracy segmentation of both cine and late gadolinium enhancement (LGE) images, while frozen models extract embedding features that, combined with an attention mechanism, enable multi-instance learning for end-to-end LVEF regression. This method represents the first effective integration of multiple foundation models, overcoming the limitations of single-model approaches. Experimental results demonstrate Dice scores of 0.862–0.902 for cine and 0.621–0.846 for LGE segmentation, with an LVEF estimation mean absolute error of 4.96% and a Pearson correlation coefficient of 0.91.
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