Self-Supervised Histology Feature Extraction for Weakly-Supervised Learning
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Solution Overview
Problem
Current machine learning approaches for histopathology rely heavily on strongly-supervised model training with detailed annotations, which are costly and time-consuming, and weakly-supervised methods using frozen feature extractors pre-trained on out-of-domain data like ImageNet, leading to suboptimal performance and GPU memory constraints.
Innovation Solution
Training a feature extractor using self-supervised learning algorithms like Momentum Contrast (MoCo) on in-domain histology tiles without annotations, performing data augmentation, and using contrastive loss to generate semantic tile embeddings, which can then be used for weakly-supervised machine learning models and fine-tuning strongly-supervised models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If strongly-supervised model training with detailed annotations is used, then model performance is improved, but annotation cost and time consumption increase
Solution Approach 1:
The system performs self-supervised learning by automatically generating training signals from the data itself through data augmentation. The feature extractor learns to produce consistent embeddings for augmented views of the same tile without requiring manual annotations, enabling the model to train itself on large volumes of unannotated histology data while maintaining high performance
Solution Approach 2:
The feature extractor is pre-trained on large amounts of unannotated data using self-supervised learning before being applied to the specific classification task. This preliminary training on abundant unannotated data prepares the model to achieve strong performance with minimal task-specific annotations
2Productivity
If frozen feature extractors pre-trained on ImageNet are used, then training speed is improved, but performance and domain adaptability deteriorate
Solution Approach 1:
The system changes the training regime from frozen pre-trained weights to trainable weights, allowing the feature extractor to adapt its parameters to the histology domain. The model is trained end-to-end with task-specific loss functions, enabling it to learn domain-relevant features while maintaining reasonable training speed
Solution Approach 2:
The feature extractor undergoes preliminary self-supervised training on histology data before being fine-tuned for the specific classification task. This two-stage approach allows the model to first learn general histology features from unannotated data, then specialize for the target task with minimal annotations
3Measurement precision
If detailed local annotations are collected from domain experts, then model accuracy is improved, but cost and complexity increase
Solution Approach 1:
The system replaces manual expert annotation with automated self-supervised learning. The model generates its own training signals by comparing embeddings of augmented views of the same tile, eliminating the need for expensive and complex manual annotations while maintaining high classification accuracy
Solution Approach 2:
Instead of relying on manual copies of annotated data, the system creates synthetic training examples through data augmentation operations on unannotated data. These augmented copies serve as positive pairs for self-supervised learning, providing abundant training signals without manual intervention
Data Source
AI summary
A method and apparatus is provided for determining one or more regions of interest in an input histology image. Such methods can include receiving an input histology image, and tiling the input histology image into a set of tiles. In various embodiments, the method can also include, for each tile, extracting a feature of that tile by applying a trained feature extractor. The trained feature extractor can be trained with an unsupervised machine learning algorithm using a training set of images. The method can also include clustering the extracted features to assign each of the set of tiles to one of a plurality of regions of interest for each tile, and outputting the plurality of regions of interest.