# Deep Learning for Taxol Exposure Analysis: A New Cell Image Dataset and Attention-Based Baseline Model

arXiv: https://arxiv.org/abs/2508.14349 · HTML: https://mlsanity.com/paper/2508.14349/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 20 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [A Metric Learning Reality Check](https://mlsanity.com/paper/2003.08505/) (2020)

## Built on by (most cited first, 1 of 1)

- [Interpretable Tile-Based Classification of Paclitaxel Exposure](https://mlsanity.com/paper/2510.23363/) (2025)

## Abstract (arXiv)

Monitoring the effects of the chemotherapeutic agent Taxol at the cellular level is critical for both clinical evaluation and biomedical research. However, existing detection methods require specialized equipment, skilled personnel, and extensive sample preparation, making them expensive, labor-intensive, and unsuitable for high-throughput or real-time analysis. Deep learning approaches have shown great promise in medical and biological image analysis, enabling automated, high-throughput assessment of cellular morphology. Yet, no publicly available dataset currently exists for automated morphological analysis of cellular responses to Taxol exposure. To address this gap, we introduce a new microscopy image dataset capturing C6 glioma cells treated with varying concentrations of Taxol. To provide an effective solution for Taxol concentration classification and establish a benchmark for future studies on this dataset, we propose a baseline model named ResAttention-KNN, which combines a ResNet-50 with Convolutional Block Attention Modules and uses a k-Nearest Neighbors classifier in the learned embedding space. This model integrates attention-based refinement and non-parametric classification to enhance robustness and interpretability. Both the dataset and implementation are publicly released to support reproducibility and facilitate future research in vision-based biomedical analysis.