Vision Transformer Learning for Processing-Agnostic Pathology Images
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Solution Overview
Problem
Conventional image analysis techniques for digital pathology are limited by the use of pre-trained convolutional neural networks on generic natural images, which are not effective for distinguishing features in whole slide images, and the lack of labeled data sets hinders supervised learning, while variations in tissue sample processing techniques introduce differences that inhibit accurate classification.
Innovation Solution
A machine learning model is trained to be agnostic to tissue sample processing techniques by generating augmented views and enhancing similarity between representations from different scanners and staining machines, using a vision transformer and discriminator to update encoder parameters based on metadata and loss computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pre-trained CNNs on generic natural images are used, then the model can be quickly deployed, but the model performs poorly on distinguishing features in whole slide images
Solution Approach 1:
The patent applies preliminary action by pre-training the vision transformer on generic natural images before fine-tuning on digital pathology images. This two-stage approach allows the model to first learn general visual representations efficiently, then adapt to the specific requirements of pathology images, resolving the contradiction between quick deployment and accurate feature distinction.
Solution Approach 2:
The patent utilizes parameter changes by transitioning from pre-trained CNN parameters to a vision transformer architecture with different parameter initialization and training regimes. The model starts with generic image parameters and progressively adapts them through fine-tuning on pathology-specific data, enabling both rapid deployment and high accuracy.
2Measurement precision
If supervised learning techniques are applied to labeled WSI datasets, then the model can achieve high accuracy, but the lack of labeled data prevents effective training
Solution Approach 1:
The patent applies self-service through self-supervised learning methods where the model generates its own training data by creating augmented views of images and learning from the relationships between these views. This eliminates the dependency on manually labeled datasets, allowing the model to train effectively without requiring extensive annotated pathology data.
Solution Approach 2:
The patent introduces an intermediary approach by using image augmentation techniques as a mediator between the raw images and the learning process. The augmented views serve as intermediate representations that enable the model to learn meaningful features without direct supervision, bridging the gap between unlabeled data and supervised learning requirements.
3Adaptability or versatility
If images from different scanners and processing techniques are used, then the model can be more versatile, but the scanner-specific features inhibit downstream classification tasks
Solution Approach 1:
The patent applies the extraction principle by separating the scanner-specific artifacts from the biologically relevant features through careful feature engineering and training. The model learns to extract and emphasize only the features that are consistent across different scanners and processing techniques, while filtering out scanner-specific noise and artifacts that would harm classification reliability.
Solution Approach 2:
The patent utilizes parameter changes by adjusting the model's feature extraction parameters and loss functions to be invariant to scanner-specific variations. Through carefully designed training objectives and parameter optimization, the model adapts to handle images from multiple scanners and processing techniques while maintaining reliable classification performance.
Data Source
AI summary
Described herein are systems, methods, and programming for analyzing and classifying digital pathology images agnostic to sample processing techniques used to prepare the digital pathology images. In some embodiments, image data including a first image set and a second image set may be obtained. The first and second image sets may be processed using a first and second slide preparation machine, respectively. A first augmented view set and a second augmented view set may be generated based on augmentations applied to the first and second image sets. For each image, a first vision transformer to may be trained to: generate a first representation of an augmented view of the first augmented view set, and enhance a similarity between the first representation and a second representation of an augmented view of the second augmented view set. The second representation may be generated via a second vision transformer.