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.

翻译：医学图像分析在药物发现和临床前评估中至关重要，其中可扩展的客观读数能够加速决策过程。我们研究了通过C6胶质瘤细胞的相差显微图像进行紫杉醇（泰素）暴露分类的任务——该任务存在细微的剂量差异，对全图像模型构成挑战。我们提出了一种简单的图块划分与聚合流程，该流程在局部图像块上操作，并将图块输出整合为图像标签，在基准数据集上实现了最先进的准确率，较已发表的基线提升了约20个百分点，且交叉验证结果证实了这一趋势。为理解图块划分的有效性，我们进一步应用了Grad-CAM、Score-CAM及注意力分析，这些方法增强了模型的可解释性，并为未来医学图像研究指明了面向鲁棒性的方向。代码已开源以促进复现与拓展。