TilGAN: GAN for Facilitating Tumor-Infiltrating Lymphocyte Pathology Image Synthesis With Improved Image...

Area of Science:

• Computational pathology
• Artificial intelligence in oncology
• Image synthesis and analysis

Background:

• Manual assessment of tumor-infiltrating lymphocytes (TILs) is error-prone and time-consuming.
• Accurate TIL quantification is vital for cancer prognosis and treatment response.
• Machine learning for TIL analysis requires extensive labeled datasets, which are scarce and costly.

Purpose of the Study:

• To develop an efficient generative adversarial network (GAN), named TilGAN, for synthetic pathology image generation.
• To enable accurate classification of TIL and non-TIL regions using generated synthetic data.
• To address the challenge of limited labeled data in computational pathology.

Main Methods:

• Proposed an efficient generative adversarial network (TilGAN) comprising generator and discriminator networks.
• Introduced novel architecture, loss functions, and evaluation techniques for TilGAN.
• Utilized TilGAN to generate synthetic pathology images and train a TIL classification model.

Main Results:

• TilGAN-generated images surpassed real images in Inception score (2.90 vs. 2.32).
• Achieved superior performance metrics: lower kernel Inception distance (1.44) and Fréchet Inception distance (0.312).
• A classification model trained on ~1 million synthetic images achieved 97.83% accuracy, 97.37% F1-score, and 97% AUC.

Conclusions:

• TilGAN effectively generates high-quality synthetic pathology images for TIL analysis.
• The proposed method significantly improves TIL classification accuracy, overcoming data scarcity.
• TilGAN demonstrates potential for broader applications in medical image synthesis.