Beyond Point Prediction: Artificial Representative Trees with Uncertainty
该研究通过结合人工代表性树(ARTs)与Mondrian保形预测系统(CPS)，解决了解释性模型的稳定性和不确定性量化问题，提供连续预测及校准概率。
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A Federated Artificial Intelligence Framework for Optimizing Pancreatic Cancer Treatment - Strategy Update
本文通过使用联邦学习架构解决胰腺癌治疗优化问题，提出一种新算法处理部分重叠特征，并分享了实施过程中的挑战和初步结果。
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Expert Consensus on Criteria for the Automated Assessment of Laparoscopic Camera Navigation
This study addresses the longstanding reliance on subjective and inefficient manual scoring in assessing laparoscopic camera navigation skills, which lacks standardized and scalable objective metrics. The authors propose a novel evaluation taxonomy comprising 14 key elements, aligning clinical importance—established through expert consensus—with technical readiness of computer vision methods via a “clinical importance–technical readiness” matrix to prioritize automation targets. Through Likert-scale surveys, expert-based skill rankings, and computer vision–derived automated measurements, validated across 23 surgeons, the study identifies high-priority metrics such as field-of-view coverage, focus quality, and instrument centering. These metrics jointly satisfy clinical relevance and technical feasibility, establishing a practical framework for AI-driven surgical training assessment.
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Empirical prior distributions for treatment-by-subgroup interaction heterogeneity in random-effects meta-analysis
This study addresses the imprecise inference in subgroup interaction meta-analyses under sparse data, which stems from the absence of empirical prior distributions tailored to interaction heterogeneity. Leveraging over 3,000 interaction meta-analyses from the Cochrane Database of Systematic Reviews, we construct the first treatment-by-subgroup interaction–specific empirical prior distribution, revealing that such interaction heterogeneity is typically substantially smaller than that of overall treatment effects. By integrating a Bayesian random-effects model with large-scale data mining and predictive prior derivation, the proposed prior markedly improves estimation accuracy in sparse-data settings compared to conventional heterogeneity priors, thereby offering a more reliable evidentiary foundation for subgroup analyses.
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Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification
This work identifies and formally names a previously unrecognized issue in hybrid quantum neural networks—“measurement-induced logit contraction”—where quantum measurement outputs, constrained to the interval [−1, 1], diminish the sensitivity of cross-entropy loss to logit differences, leading to vanishing gradients and unstable training. To address this, the authors propose a circuit-agnostic, learnable Quantum Measurement Temperature (QMT) mechanism that adaptively scales measurement outputs to enhance loss sensitivity without altering the underlying quantum circuit architecture. Experimental results demonstrate that QMT substantially improves logit separation, gradient magnitude, and training stability, yielding higher classification accuracy on both fluorescence microscopy images and a six-class Fashion-MNIST benchmark.
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Beyond Point Prediction: Artificial Representative Trees with Uncertainty
该研究通过结合人工代表性树(ARTs)与Mondrian保形预测系统(CPS)，解决了解释性模型的稳定性和不确定性量化问题，提供连续预测及校准概率。
0 citationsRead paper
A Federated Artificial Intelligence Framework for Optimizing Pancreatic Cancer Treatment - Strategy Update
本文通过使用联邦学习架构解决胰腺癌治疗优化问题，提出一种新算法处理部分重叠特征，并分享了实施过程中的挑战和初步结果。
0 citationsRead paper
Expert Consensus on Criteria for the Automated Assessment of Laparoscopic Camera Navigation
This study addresses the longstanding reliance on subjective and inefficient manual scoring in assessing laparoscopic camera navigation skills, which lacks standardized and scalable objective metrics. The authors propose a novel evaluation taxonomy comprising 14 key elements, aligning clinical importance—established through expert consensus—with technical readiness of computer vision methods via a “clinical importance–technical readiness” matrix to prioritize automation targets. Through Likert-scale surveys, expert-based skill rankings, and computer vision–derived automated measurements, validated across 23 surgeons, the study identifies high-priority metrics such as field-of-view coverage, focus quality, and instrument centering. These metrics jointly satisfy clinical relevance and technical feasibility, establishing a practical framework for AI-driven surgical training assessment.
0 citationsRead paper
Empirical prior distributions for treatment-by-subgroup interaction heterogeneity in random-effects meta-analysis
This study addresses the imprecise inference in subgroup interaction meta-analyses under sparse data, which stems from the absence of empirical prior distributions tailored to interaction heterogeneity. Leveraging over 3,000 interaction meta-analyses from the Cochrane Database of Systematic Reviews, we construct the first treatment-by-subgroup interaction–specific empirical prior distribution, revealing that such interaction heterogeneity is typically substantially smaller than that of overall treatment effects. By integrating a Bayesian random-effects model with large-scale data mining and predictive prior derivation, the proposed prior markedly improves estimation accuracy in sparse-data settings compared to conventional heterogeneity priors, thereby offering a more reliable evidentiary foundation for subgroup analyses.
0 citationsRead paper
Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification
This work identifies and formally names a previously unrecognized issue in hybrid quantum neural networks—“measurement-induced logit contraction”—where quantum measurement outputs, constrained to the interval [−1, 1], diminish the sensitivity of cross-entropy loss to logit differences, leading to vanishing gradients and unstable training. To address this, the authors propose a circuit-agnostic, learnable Quantum Measurement Temperature (QMT) mechanism that adaptively scales measurement outputs to enhance loss sensitivity without altering the underlying quantum circuit architecture. Experimental results demonstrate that QMT substantially improves logit separation, gradient magnitude, and training stability, yielding higher classification accuracy on both fluorescence microscopy images and a six-class Fashion-MNIST benchmark.
0 citationsRead paper