SCoPE-Reg: Efficient Rigid Ultrasound Slice-to-Volume Registration via State-Space Correlation and Closed-Form Pose Estimation
本文提出SCoPE-Reg方法，通过状态空间相关性和无参数加权Kabsch估计解决超声图像刚性切片到体积配准问题，提高精度和效率。
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It depends: Incorporating correlations for joint aleatoric and epistemic uncertainties of high-dimensional output spaces
本文提出一种新方法，通过低秩加对角协方差结构联合建模高维输出空间中的偶然和认知不确定性，以提高深度学习模型预测的可靠性。
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Concept-based explanation of gene expression prediction from H&E images
This study addresses the limitation of existing Vision Transformer interpretability methods in elucidating how morphological concepts contribute to spatial transcriptomics predictions. We propose a concept graph framework integrating Layer-wise Relevance Propagation with Top-K Sparse Autoencoders to enable global morphology-molecular association analysis from H&E images to gene expression, overcoming the constraints of local heatmaps. Experimental results demonstrate that the model achieves an F1 score of 0.872 in iCMS classification, effectively stratifies patient prognosis, and exhibits robust cross-dataset generalizability. Consequently, this work establishes a novel interpretable paradigm for understanding the intrinsic mechanisms linking tissue morphology to transcriptional programs, providing critical insights into the molecular underpinnings of histopathological features.
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SCoPE-Reg: Efficient Rigid Ultrasound Slice-to-Volume Registration via State-Space Correlation and Closed-Form Pose Estimation
本文提出SCoPE-Reg方法，通过状态空间相关性和无参数加权Kabsch估计解决超声图像刚性切片到体积配准问题，提高精度和效率。
0 citationsRead paper
LeVJEPA: Efficient & Scalable Video Pretraining without the Heuristics
本文提出LeVJEPA，通过无崩溃目标和SIGReg正则化方法有效解决视频预训练计算成本高的问题，同时保持下游任务准确性。
0 citationsRead paper
It depends: Incorporating correlations for joint aleatoric and epistemic uncertainties of high-dimensional output spaces
本文提出一种新方法，通过低秩加对角协方差结构联合建模高维输出空间中的偶然和认知不确定性，以提高深度学习模型预测的可靠性。
0 citationsRead paper
Concept-based explanation of gene expression prediction from H&E images
This study addresses the limitation of existing Vision Transformer interpretability methods in elucidating how morphological concepts contribute to spatial transcriptomics predictions. We propose a concept graph framework integrating Layer-wise Relevance Propagation with Top-K Sparse Autoencoders to enable global morphology-molecular association analysis from H&E images to gene expression, overcoming the constraints of local heatmaps. Experimental results demonstrate that the model achieves an F1 score of 0.872 in iCMS classification, effectively stratifies patient prognosis, and exhibits robust cross-dataset generalizability. Consequently, this work establishes a novel interpretable paradigm for understanding the intrinsic mechanisms linking tissue morphology to transcriptional programs, providing critical insights into the molecular underpinnings of histopathological features.
0 citationsRead paper