Unified Framework for Histopathology Image Augmentation and Classification via Generative Models

• URL: http://arxiv.org/abs/2212.09977v2
• Date: Sun, 13 Oct 2024 08:59:08 GMT
• Title: Unified Framework for Histopathology Image Augmentation and Classification via Generative Models
• Authors: Meng Li, Chaoyi Li, Can Peng, Brian C. Lovell,
• Abstract summary: We propose an innovative unified framework that integrates the data generation and model training stages into a unified process. Our approach utilizes a pure Vision Transformer (ViT)-based conditional Generative Adversarial Network (cGAN) model to simultaneously handle both image synthesis and classification. Our experiments show that our unified synthetic augmentation framework consistently enhances the performance of histopathology image classification models.
• Score: 6.404713841079193
• License:
• Abstract: Deep learning techniques have become widely utilized in histopathology image classification due to their superior performance. However, this success heavily relies on the availability of substantial labeled data, which necessitates extensive and costly manual annotation by domain experts. To address this challenge, researchers have recently employed generative models to synthesize data for augmentation, thereby enhancing classification model performance. Traditionally, this involves generating synthetic data first and then training the classification model with both synthetic and real data, which creates a two-stage, time-consuming workflow. To overcome this limitation, we propose an innovative unified framework that integrates the data generation and model training stages into a unified process. Our approach utilizes a pure Vision Transformer (ViT)-based conditional Generative Adversarial Network (cGAN) model to simultaneously handle both image synthesis and classification. An additional classification head is incorporated into the cGAN model to enable simultaneous classification of histopathology images. To improve training stability and enhance the quality of generated data, we introduce a conditional class projection technique that helps maintain class separation during the generation process. We also employ a dynamic multi-loss weighting mechanism to effectively balance the losses of the classification tasks. Furthermore, our selective augmentation mechanism actively selects the most suitable generated images for data augmentation to further improve performance. Extensive experiments on histopathology datasets show that our unified synthetic augmentation framework consistently enhances the performance of histopathology image classification models.

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