Are You Learning Biological Signal or Shortcuts? Auditing and Mitigating Bias in Protein-Protein Interaction Datasets
研究通过分析PPI数据库中的偏差，提出一种结合相似性感知的数据分割和基于优化的负采样方法，以减少机器学习模型在预测蛋白质相互作用时对非生物信号的学习。
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Signal-based AI-driven software solution for automated quantification of metastatic bone disease and treatment response assessment using Whole-Body Diffusion-Weighted MRI (WB-DWI) biomarkers in Advanced Prostate Cancer
This study addresses the challenge of quantitative assessment of bone metastatic burden and treatment response in advanced prostate cancer. Methodologically, we propose a fully automated AI framework based on whole-body diffusion-weighted MRI (WB-DWI): (1) a weakly supervised Residual U-Net generates skeletal probability maps to guide lesion detection; (2) a WB-DWI intensity statistical normalization strategy is introduced; and (3) a lightweight CNN enables end-to-end lesion segmentation, followed by registration with gADC maps to extract tumor diffusion volume (TDV) and median gADC—key quantitative biomarkers. Our key contribution is enabling bone metastasis quantification without per-lesion annotation. Validation demonstrates skeletal segmentation Dice scores of 0.6 (pelvis/spine), coefficient of variation (CV) of 4.6% for log-TDV and 3.6% for median gADC, and treatment response classification accuracy of 80.5%, sensitivity of 84.3%, and specificity of 85.7%. Processing time per case is 90 seconds.
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Are You Learning Biological Signal or Shortcuts? Auditing and Mitigating Bias in Protein-Protein Interaction Datasets
研究通过分析PPI数据库中的偏差，提出一种结合相似性感知的数据分割和基于优化的负采样方法，以减少机器学习模型在预测蛋白质相互作用时对非生物信号的学习。
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Signal-based AI-driven software solution for automated quantification of metastatic bone disease and treatment response assessment using Whole-Body Diffusion-Weighted MRI (WB-DWI) biomarkers in Advanced Prostate Cancer
This study addresses the challenge of quantitative assessment of bone metastatic burden and treatment response in advanced prostate cancer. Methodologically, we propose a fully automated AI framework based on whole-body diffusion-weighted MRI (WB-DWI): (1) a weakly supervised Residual U-Net generates skeletal probability maps to guide lesion detection; (2) a WB-DWI intensity statistical normalization strategy is introduced; and (3) a lightweight CNN enables end-to-end lesion segmentation, followed by registration with gADC maps to extract tumor diffusion volume (TDV) and median gADC—key quantitative biomarkers. Our key contribution is enabling bone metastasis quantification without per-lesion annotation. Validation demonstrates skeletal segmentation Dice scores of 0.6 (pelvis/spine), coefficient of variation (CV) of 4.6% for log-TDV and 3.6% for median gADC, and treatment response classification accuracy of 80.5%, sensitivity of 84.3%, and specificity of 85.7%. Processing time per case is 90 seconds.
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