BYOL Self-Supervised Learning for Digital Pathology Feature Extraction
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Solution Overview
Problem
Conventional image analysis techniques for digital pathology, such as those using convolutional neural networks (CNNs) pre-trained on generic natural images, are ineffective for analyzing whole slide images (WSIs) due to their generic nature and the lack of labeled data sets, making it difficult to apply supervised learning techniques effectively.
Innovation Solution
The BYOL self-supervised image representation learning technique is adapted for digital pathology by using an augmented view of an image to train one network to predict a target network's representation, eliminating the need for negative pairs and achieving high accuracy through the use of custom image augmentations like rotation, scaling, and color jittering, which forces the network to learn features specific to digital pathology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If CNNs pre-trained on generic natural images are used for digital pathology image analysis, then the model can be quickly deployed, but the feature extraction accuracy is poor because generic natural images do not capture the specific characteristics of pathology images
Solution Approach 1:
The patent applies self-supervised learning to pre-train CNNs on large quantities of unlabeled pathology images before fine-tuning on smaller labeled datasets. This preliminary pre-training action enables the model to learn domain-specific features from the pathology image domain, resolving the contradiction by maintaining quick deployment capability while significantly improving feature extraction accuracy through domain-adapted preliminary learning
Solution Approach 2:
The patent modifies the training parameters and data characteristics by using self-supervised learning objectives (such as contrastive learning) that are specifically tailored for pathology images. This changes the learning parameters from generic natural image features to pathology-specific features, enabling the model to capture domain-specific characteristics while maintaining efficient deployment
2Measurement precision
If supervised learning techniques are applied to train CNNs on pathology images, then feature extraction accuracy can be improved, but the process becomes difficult because most large pathology datasets are not labeled
Solution Approach 1:
The patent implements self-supervised learning where the model learns to extract meaningful features from unlabeled pathology images by creating its own supervision signals through data augmentations and reconstruction tasks. This self-service approach eliminates the need for manual labeling while maintaining high feature extraction accuracy, directly resolving the contradiction between accuracy improvement and labeling complexity
Solution Approach 2:
The patent introduces self-supervised learning objectives as an intermediary mechanism that bridges the gap between unlabeled data and supervised learning requirements. By using pretext tasks such as image reconstruction or contrastive learning on augmented views, the model indirectly learns supervised-like features without requiring explicit labels, thus resolving the contradiction
3Adaptability or versatility
If scale-invariant features are used in natural image processing, then the model can handle varying object scales, but this is undesirable in digital pathology where fixed scale/magnification is required to retain object-size-related features for cell type discrimination
Solution Approach 1:
The patent inverts the conventional approach by deliberately avoiding scale-invariant feature extraction and instead focusing on scale-sensitive learning. By training the model at fixed magnifications and preserving object-size-related features, the model can accurately discriminate between cell types based on their characteristic sizes, resolving the contradiction by inverting the scale handling strategy from natural image processing
4Measurement precision
If custom image augmentations are applied to force the network to learn pathology-specific features, then feature recognition accuracy improves, but the training complexity and computational cost increase
Solution Approach 1:
The patent extracts and applies only the necessary custom image augmentations specific to pathology domains, such as color jittering appropriate for H&E stains and spatial transformations relevant to tissue structures. By selectively taking out and applying only the essential augmentations rather than all possible transformations, the model achieves high feature recognition accuracy while controlling training complexity
Data Source
AI summary
Described herein are systems, methods, and programming for analyzing and classifying digital pathology images. Some embodiments include receiving whole slide images (WSIs) and dividing each of the WSIs into tiles. For each WSI, a random subset of the tiles may be selected and augmented views of each of the selected tiles may be generated. For each of the selected tiles, a first convolutional neural network (CNN) may be trained to: generate, using a first one of the augmented views corresponding to the selected tile, a first representation of the selected tile, and predict a second representation of the selected tile to be generated by a second CNN, wherein the second representation is generated based on a second one of the augmented views of the selected tile.