Masked Pre-Training of Transformers for Histology Image Analysis

• URL: http://arxiv.org/abs/2304.07434v1
• Date: Fri, 14 Apr 2023 23:56:49 GMT
• Title: Masked Pre-Training of Transformers for Histology Image Analysis
• Authors: Shuai Jiang, Liesbeth Hondelink, Arief A. Suriawinata, Saeed Hassanpour
• Abstract summary: In digital pathology, whole slide images (WSIs) are widely used for applications such as cancer diagnosis and prognosis prediction. Visual transformer models have emerged as a promising method for encoding large regions of WSIs while preserving spatial relationships among patches. We propose a pretext task for training the transformer model without labeled data to address this problem. Our model, MaskHIT, uses the transformer output to reconstruct masked patches and learn representative histological features based on their positions and visual features.
• Score: 4.710921988115685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In digital pathology, whole slide images (WSIs) are widely used for applications such as cancer diagnosis and prognosis prediction. Visual transformer models have recently emerged as a promising method for encoding large regions of WSIs while preserving spatial relationships among patches. However, due to the large number of model parameters and limited labeled data, applying transformer models to WSIs remains challenging. Inspired by masked language models, we propose a pretext task for training the transformer model without labeled data to address this problem. Our model, MaskHIT, uses the transformer output to reconstruct masked patches and learn representative histological features based on their positions and visual features. The experimental results demonstrate that MaskHIT surpasses various multiple instance learning approaches by 3% and 2% on survival prediction and cancer subtype classification tasks, respectively. Furthermore, MaskHIT also outperforms two of the most recent state-of-the-art transformer-based methods. Finally, a comparison between the attention maps generated by the MaskHIT model with pathologist's annotations indicates that the model can accurately identify clinically relevant histological structures in each task.

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