Out-of-Distribution Detection in Histopathology Using Normal Tissue Classes
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Solution Overview
Problem
Traditional supervised learning techniques for anomaly detection in histopathology face challenges due to the difficulty in creating comprehensive labeled datasets for rare and diverse abnormalities, leading to high misclassification rates and inefficiencies in capturing the complexity of normal tissue structures.
Innovation Solution
A multi-class in-distribution model is employed to learn the distribution of different normal tissue classes using contrastive learning, enabling robust anomaly detection by distinguishing multiple normal tissue subtypes and reducing overlap between classes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If supervised learning techniques are used for anomaly detection, then the model can learn decision boundaries for known anomalies, but it fails to generalize to novel anomalies and requires extensive labeled data that is difficult to obtain
Solution Approach 1:
The patent inverts the traditional supervised learning approach by training the model exclusively on normal tissue images rather than abnormal ones. The model learns what normal tissue should look like and automatically identifies deviations as anomalies, eliminating the need for labeled abnormal data while improving generalization to novel anomaly types.
Solution Approach 2:
The system performs self-service by automatically detecting anomalies without requiring expert pathologist annotation of abnormal cases. The model trains itself on readily available normal tissue images and autonomously identifies out-of-distribution patterns, reducing dependency on scarce labeled abnormal data.
2Device complexity
If a One-Class Classifier is used to treat all normal tissue as a single category, then the model simplifies the learning task, but it fails to capture the complexity and heterogeneity of normal tissue structures leading to misclassifications
Solution Approach 1:
The patent segments normal tissue into multiple distinct classes based on tissue type, organ, or morphological characteristics. This multi-class segmentation allows the model to capture the heterogeneity and complexity of normal tissue structures while maintaining a structured approach to learning different tissue variations.
Solution Approach 2:
The model applies local quality by learning tissue-specific characteristics for each normal class separately. Each tissue type is represented with its own unique features and distribution patterns, allowing the model to accurately capture local variations in normal tissue morphology rather than forcing a single global representation.
3Ease of manufacture
If supervised models are trained on limited abnormal cases, then the training process becomes feasible, but the model fails to detect rare and diverse abnormal scenarios effectively
Solution Approach 1:
The patent inverts the training data requirement by using only normal tissue images for training, which are abundant and easily obtainable. This approach eliminates the bottleneck of collecting and labeling rare abnormal cases while achieving comprehensive anomaly detection coverage through out-of-distribution identification.
Solution Approach 2:
The system achieves self-service by automatically identifying anomalies without requiring labeled abnormal training data. The model leverages the plentiful normal tissue data to learn the expected distribution and autonomously detects deviations, making the training process feasible while maintaining high detection coverage.
Data Source
AI summary
Embodiments herein disclose methods and ystems for performing unsupervised out-of-distribution detection of abnormal regions in histopathology media using multi-class in-distribution modelling. Embodiments herein disclose methods and systems for automatically identifying abnormal regions in a tissue whole slide media by utilizing a multi-class normal representation that is learned exclusively from normal tissue whole slide media.