Interpretable Tile-Based Classification of Paclitaxel Exposure
This addresses drug discovery by providing a scalable, interpretable method for medical image analysis, though it appears incremental.
The paper tackled classification of paclitaxel exposure from microscopy images of glioma cells, achieving state-of-the-art accuracy with a 20 percentage point improvement over the baseline.
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.