Patch-level Tumor Classification in Digital Histopathology Images with Domain Adapted Deep Learning

Area of Science:

• Computational pathology
• Medical image analysis
• Deep learning in oncology

Background:

• Manual tumor histopathology is vital for cancer diagnosis but is subjective and labor-intensive.
• Deep learning, particularly convolutional neural networks (CNNs), shows promise in medical imaging but requires large, domain-specific annotated datasets.
• Acquiring such datasets for histopathology is challenging due to variations in tissue types and annotation complexity.

Purpose of the Study:

• To address the scarcity of annotated histopathology data for specific cancer types.
• To develop a domain adaptation strategy for improving CNN-based histopathology image classification.
• To leverage existing annotated data from different tissue types for enhanced model training.

Main Methods:

• Utilized domain adaptation by pre-training CNNs on annotated histopathology images from various tissue types.
• Developed a shared histopathology domain representation focusing on common features like stains and cellular structures.
• Fine-tuned the pre-trained models for classification tasks on specific, limited-data histopathology domains.

Main Results:

• The domain adaptation approach significantly improved classification accuracy compared to training CNNs from scratch on limited data (84.3% vs. 78.3%).
• Achieved a higher area under the receiver operating characteristic curve (AUC) with the proposed method (0.918 vs. 0.867).
• Demonstrated the effectiveness of transferring knowledge across different histopathology domains.

Conclusions:

• Domain adaptation is a viable and effective strategy for overcoming data limitations in histopathology image classification.
• Pre-training on diverse datasets enables robust feature learning, enhancing performance on specific tissue types.
• This approach holds potential for improving the accuracy and efficiency of computational pathology tools in cancer diagnosis.