Tissue Specimen Classification in Digital Whole Slide Images
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Solution Overview
Problem
In histopathology, digital whole slide images often contain multiple tissue specimen types or incorrect recordings, leading to inaccurate AI-based image processing due to mismatches between recorded and actual tissue types, causing erroneous outputs.
Innovation Solution
A system and method using trained machine learning systems and image processing techniques to classify tissue specimen types within digital whole slide images, identifying in-distribution and out-of-distribution tiles by extracting feature vectors and determining their probability matches with recorded types, thereby correcting tissue type mismatches and applying appropriate AI systems for accurate processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single tissue specimen type is assumed for the entire slide based on database recording, then processing simplicity is maintained, but classification accuracy deteriorates due to erroneous recordings and undetected multiple tissue types
Solution Approach 1:
The digital whole slide image is divided into multiple tiles, and each tile is independently classified to determine its tissue specimen type. This segmentation allows the system to handle different tissue types within the same slide by processing each tile separately, thereby resolving the contradiction between processing simplicity and classification accuracy.
Solution Approach 2:
The system changes the parameter of tissue type assignment from a single global label to multiple local labels per tile. By evaluating probability scores and comparing them against threshold values, the system dynamically adjusts the classification outcome for each tile based on its actual content rather than relying on a potentially erroneous database recording.
2Productivity
If AI-based image processing is applied without verifying tissue type matches, then processing speed is maintained, but diagnostic reliability deteriorates due to incorrect tissue type assumptions
Solution Approach 1:
The system performs preliminary classification of each tile's tissue specimen type before applying AI-based image processing. By extracting feature vectors and calculating probability scores in advance, the system ensures that the correct tissue type-specific AI model is selected, thereby maintaining processing speed while improving diagnostic reliability.
Solution Approach 2:
The system uses probability scores from the classification model as feedback to determine whether to apply the recorded tissue type or select an alternative. When the probability score exceeds a threshold, the system confidently applies the corresponding AI model; otherwise, it seeks alternative tissue type matches, ensuring reliable processing without sacrificing speed.
3Measurement precision
If multiple tissue specimen types are detected and classified per slide, then classification accuracy is improved, but system complexity increases due to additional processing steps
Solution Approach 1:
The system employs a universal machine learning classification model that can handle multiple tissue specimen types through a single unified architecture. By training the model on diverse tissue types and using probability-based classification, the system achieves multi-functionality without requiring separate specialized systems for each tissue type, thereby improving classification accuracy while controlling system complexity.
Data Source
AI summary
Systems and methods are disclosed for identifying tissue specimen types present in digital whole slide images. In some aspects, tissue specimen types may be identified using unsupervised machine learning techniques for out-of-distribution detection. For example, a digital whole slide image of a tissue specimen and a recorded tissue specimen type for the digital whole slide image may be received. One or more feature vectors may be extracted from one or more foreground tiles of the digital whole slide image identified as including the tissue specimen, and a distribution learned by a machine learning system for the recorded tissue specimen type may be received. Using the distribution, a probability of the feature vectors corresponding to the recorded tissue specimen type may be computed and used as a basis for classifying the foreground tiles from which the feature vectors are extracted as an in-distribution foreground tile or an out-of-distribution foreground tile.