Multi-Class Token ViT for Unsupervised Histopathology Phenotyping
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Solution Overview
Problem
The scarcity and high cost of labeled data in medical imaging, particularly in histopathology, limit the development of accurate AI models, and existing unsupervised learning methods struggle to capture semantically meaningful features in digital pathology images due to differences between natural and medical images.
Innovation Solution
A self-supervised hierarchical Vision Transformer (ViT) model with multi-class tokens is used to perform unsupervised clustering, capturing fine-grained regions of interest in digital pathology images, leveraging unlabeled data to learn domain-specific features without manual labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If unsupervised learning methods are used to process digital pathology images, then the need for manual labeling is reduced, but the ability to capture semantically meaningful features deteriorates due to disparities between natural and medical image features
Solution Approach 1:
The model performs preliminary action by pre-training on natural images before fine-tuning on medical images. This pre-training phase enables the model to learn general visual features and patterns from abundant natural image data, establishing a strong foundation that improves subsequent performance on medical pathology images without requiring extensive labeled medical data.
Solution Approach 2:
The patent introduces an intermediary approach by using a two-stage training process with intermediate representations. The model creates intermediate feature representations from natural images that serve as a bridge to medical image analysis, allowing gradual adaptation from natural to medical domains and improving feature extraction quality in the target domain.
2Productivity
If transfer learning from ImageNet is used, then model training is accelerated with pre-existing knowledge, but convergence is impeded due to feature disparities between natural and medical images
Solution Approach 1:
The patent applies dynamics by using adaptive learning rates that adjust during the training process. The learning rate is dynamically modified based on training progress and domain differences, allowing the model to quickly capture general features from natural images while gradually adapting to medical image specifics, thus balancing training speed with convergence reliability.
Solution Approach 2:
The model employs parameter changes by modifying training parameters such as learning rate, weight decay, and temperature coefficients during the transition from natural to medical image domains. These parameter adjustments enable the model to leverage pre-trained knowledge while adapting to domain-specific characteristics, resolving the convergence issue.
3Measurement precision
If gigapixel tissue specimen images are processed, then diagnostic detail is improved, but labeling effort and computational requirements increase significantly
Solution Approach 1:
The patent applies segmentation by dividing gigapixel tissue images into smaller patches or tiles that can be processed independently. This segmentation strategy reduces the computational burden on each individual processing unit while maintaining the ability to analyze fine-grained diagnostic features across the entire large-scale image through hierarchical processing.
Solution Approach 2:
The model transitions from processing images at a single resolution level to a multi-scale dimensional approach. By extracting features at different resolutions and scales, the system can capture diagnostic details in gigapixel images without requiring uniform processing of the entire image at maximum resolution, thus reducing overall computational complexity.
Data Source
AI summary
The system and method for processing a digital pathology image using a machine learning model that includes a self-supervised hierarchical Vision Transformer (ViT) configured to perform unsupervised clustering with multiple classification tokens. The method includes receiving a digital pathology image that depicts a tissue slice stained with histological dyes. The digital pathology image may be processed to generate a result comprising multiple predicted classifications of individual patches of the digital pathology image. The result is generated by a machine-learning model using a self-supervised hierarchical Vision Transformer (ViT) that may further comprise a multi-head self-attention module configured to predict a crosspatch relevance metric using an attention mechanism for each individual patch in the digital pathology image thereby assigning the individual patches to a cluster based on the crosspatch relevance metrics.