用局部图像块分类紫杉醇暴露，准确率提升20个百分点。
Interpretable Tile-Based Classification of Paclitaxel Exposure
- 将显微图像切块后聚合判断，避免全图建模偏差。
- 在基准数据集上准确率达新高，比基线提升约20%。
- 结合注意力分析提升可解释性，适合医学影像研究者。
医学图像分析在药物发现和临床前评估中至关重要，可扩展、客观的读数能加速决策。本文针对从C6胶质瘤细胞的相位对比显微图像中分类紫杉醇（Taxol）暴露这一任务，该任务因剂量差异细微而挑战全图模型。我们提出一种简单的切块-聚合流程，基于局部图像块进行处理，并将块输出整合为图像标签，在基准数据集上达到当前最优准确率，相比已发表基线提升约20个百分点，交叉验证结果验证了趋势。为进一步理解切块有效的原因，我们应用Grad-CAM和Score-CAM及注意力分析，增强了模型可解释性，并指向未来以鲁棒性为导向的医学图像研究方向。代码已开源，便于复现与扩展。
原文摘要 · Abstract (English)
Medical image analysis is central to drug discovery and preclinical evaluation, where scalable, objective readouts can accelerate decision-making. We address classification of paclitaxel (Taxol) exposure from phase-contrast microscopy of C6 glioma cells -- a task with subtle dose differences that challenges full-image models. We propose a simple tiling-and-aggregation pipeline that operates on local patches and combines tile outputs into an image label, achieving state-of-the-art accuracy on the benchmark dataset and improving over the published baseline by around 20 percentage points, with trends confirmed by cross-validation. To understand why tiling is effective, we further apply Grad-CAM and Score-CAM and attention analyses, which enhance model interpretability and point toward robustness-oriented directions for future medical image research. Code is released to facilitate reproduction and extension.
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