Interpretable Tile-Based Classification of Paclitaxel Exposure

• URL: http://arxiv.org/abs/2510.23363v2
• Date: Wed, 05 Nov 2025 16:02:05 GMT
• Title: Interpretable Tile-Based Classification of Paclitaxel Exposure
• Abstract summary: We address classification of paclitaxel exposure from phase-contrast microscopy of C6 glioma cells.<n>We propose a simple tiling-and-aggregation pipeline that operates on local patches and combines tile outputs into an image label.
• Score: 2.857948740800973
• License: http://creativecommons.org/licenses/by/4.0/
• 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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