Self-Learning Tissue Fingerprints for Low-Annotation Biomarker Prediction
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Solution Overview
Problem
Deep learning methods for histopathology face challenges in obtaining large, well-annotated training sets and are prone to learning spurious features due to noise in clinical annotations, limiting their ability to predict biomarkers and theragnosis in pathology.
Innovation Solution
A machine learning technique involving pre-training neural networks to learn histology-optimized features through tissue fingerprinting, which minimizes an objective loss function to match digital image patches from the same sample while being invariant to pathology artifacts, enabling classification of stained tissue samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning is used to learn patterns from histopathological images, then classification accuracy can be improved, but large amounts of well-annotated training data are required which are difficult to obtain
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on a large volume of unannotated histopathological images to learn general histological features and tissue architecture patterns before fine-tuning on smaller annotated datasets. This pre-training phase enables the network to develop robust feature representations that transfer effectively to downstream classification tasks with limited labeled data.
2Measurement precision
If deep learning models are trained on clinical pathology datasets, then biomarker prediction capability can be enhanced, but noise in clinical annotations causes the network to learn spurious features
Solution Approach 1:
The patent extracts and removes spurious features from the learning process by employing data augmentation techniques that specifically target and eliminate artifacts such as stain color variations, imaging platform differences, and technical variations. The network is trained to focus on biologically relevant morphological features while being made invariant to these confounding factors through careful preprocessing and loss function design.
Solution Approach 2:
The patent introduces an intermediary representation layer that separates the learning of invariant histological features from the learning of annotation-specific patterns. By using intermediate feature representations and domain adaptation techniques, the network learns a shared feature space that is independent of annotation noise while preserving biologically meaningful variations.
3Adaptability or versatility
If traditional deep learning approaches are used for histopathology classification, then existing patterns can be recognized, but novel biological insights and theragnosis capabilities are limited
Solution Approach 1:
The patent applies dynamics by implementing a multi-stage training paradigm that adapts the network's learning objectives at different phases. The system dynamically transitions from learning general histological features in unannotated data to learning task-specific patterns in annotated data, and finally to discovering novel biological relationships through self-supervised learning on test data. This dynamic adaptation enables the network to uncover previously unknown biological patterns.
Data Source
AI summary
Histologic classification of pathology specimens through machine learning is a nascent field which offers tremendous potential to improve cancer medicine. Its utility has been limited, in part because of differences in tissue preparation and the relative paucity of well-annotated images. We introduce tissue recognition, an unsupervised learning problem analogous to human face recognition, in which the goal is to identify individual tumors using a learned set of histologic features. This feature set is the “tissue fingerprint.” Because only specimen identities are matched to fingerprints, constructing an algorithm for producing them is a self-learning task that does not need image metadata annotations. Here, we provide an algorithm for self-learning tissue fingerprints, that, in conjunction with color normalization, can match hematoxylin and eosin stained tissues to one of 104 patients with 93% accuracy. We applied this identification network's internal representation as a tissue fingerprint for use in predicting the molecular status of an individual tumor (breast cancer clinical estrogen receptor (ER) status). We describe a fingerprint-based classifier that predicts ER status from whole-slides with high accuracy (AUROC=0.90), and is an improvement over traditional transfer learning approaches. The use of tissue fingerprinting for digital pathology as a concise but meaningful histopathologic image representation enables a new range of machine learning algorithms leading to increased information for clinical decision making in patient management.