Interpretable Tile-Based Classification of Paclitaxel Exposure
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911249890017280 |
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| author | Fletcher, Sean Scott, Gabby Currie, Douglas Zhang, Xin Song, Yuqi MacLeod, Bruce |
| author_facet | Fletcher, Sean Scott, Gabby Currie, Douglas Zhang, Xin Song, Yuqi MacLeod, Bruce |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23363 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interpretable Tile-Based Classification of Paclitaxel Exposure Fletcher, Sean Scott, Gabby Currie, Douglas Zhang, Xin Song, Yuqi MacLeod, Bruce Computer Vision and Pattern Recognition 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. |
| title | Interpretable Tile-Based Classification of Paclitaxel Exposure |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.23363 |