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

2025-08-19

AI Summary

To address the lack of publicly available datasets and automated analysis tools for characterizing cellular morphological responses to paclitaxel (Taxol) treatment, this study introduces the first open-access microscopic image dataset of C6 glioma cells under varying Taxol concentrations. We propose ResAttention-KNN, a novel deep learning framework integrating a ResNet-50 backbone, Convolutional Block Attention Module (CBAM), and a k-nearest neighbors (k-NN) classifier, enabling robust, interpretable, and concentration-dependent morphological classification in deep feature space. Experimental results demonstrate strong generalization across diverse Taxol concentrations. The dataset, source code, and trained models are fully open-sourced. This work establishes a new benchmark and practical toolkit for visual, high-throughput, and cost-effective assessment of chemotherapeutic drug effects on cellular morphology.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Other Foundations of Computer Vision
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Automated morphological analysis of cellular responses to Taxol exposure
• Addressing lack of public dataset for Taxol-treated cell image analysis
• Developing robust classification model for Taxol concentration assessment

Innovation

• New microscopy image dataset for Taxol exposure
• ResNet-50 with attention modules integration
• K-Nearest Neighbors classifier in embedding space