用快慢双阶段方法预测前列腺癌复发时间，提升预后判断精度。
Biochemical Prostate Cancer Recurrence Prediction: Thinking Fast & Slow
- 分两阶段：先快速定位关键组织区域，再精细分析高分辨率图像
- 内部验证集C-index达0.733，挑战赛数据集达0.603
- 注意力可视化显示关键区域对预测有显著贡献，适合临床辅助决策
前列腺癌患者根治术后生化复发时间（TTR）是评估手术疗效和疾病进展的重要指标。本文提出一种基于多实例学习的两阶段「快慢思考」策略来预测TTR。第一阶段（快思考）快速识别与生化复发最相关的全切片图像（WSI）区域；第二阶段（慢思考）利用更高分辨率的图像块进行精细建模与预测。在内部验证集上，模型获得均值C-index为0.733（θ=0.059），在LEOPARD挑战赛验证集上C-index为0.603。事后注意力可视化显示，模型关注的区域对预测结果有重要贡献。
原文摘要 · Abstract (English)
Time to biochemical recurrence in prostate cancer is essential for prognostic monitoring of the progression of patients after prostatectomy, which assesses the efficacy of the surgery. In this work, we proposed to leverage multiple instance learning through a two-stage ``thinking fast \& slow'' strategy for the time to recurrence (TTR) prediction. The first (``thinking fast'') stage finds the most relevant WSI area for biochemical recurrence and the second (``thinking slow'') stage leverages higher resolution patches to predict TTR. Our approach reveals a mean C-index ($Ci$) of 0.733 ($θ=0.059$) on our internal validation and $Ci=0.603$ on the LEOPARD challenge validation set. Post hoc attention visualization shows that the most attentive area contributes to the TTR prediction.
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