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arXiv · 2510.23363

Interpretable Tile-Based Classification of Paclitaxel Exposure

Sean Fletcher·Gabby Scott·Douglas Currie·Xin Zhang·Yuqi Song·Bruce MacLeod
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Abstract

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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Sean Fletcher, Gabby Scott, Douglas Currie, Xin Zhang, Yuqi Song, Bruce MacLeod. 2025-10-27. Interpretable Tile-Based Classification of Paclitaxel Exposure. https://arxiv.org/abs/2510.23363

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