Multi-Scale Tissue Image Learning for Gene Alteration Prediction
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Solution Overview
Problem
Existing methods for predicting clinical information from histological images are laborious, prone to human error, and time-consuming, and rely heavily on the expertise of limited medical professionals, making it challenging to accurately infer subtle differences in tissue morphology and immunohistochemical staining patterns.
Innovation Solution
A machine learning-based approach using multiple instance learning models trained on histological image patches of varying spatial resolutions, which can predict gene alteration states from needle core biopsy samples by processing whole slide images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to infer clinical information from histological images, then productivity and accuracy are improved, but device complexity increases
Solution Approach 1:
The system segments the histological image into multiple patches at different spatial resolutions. Each patch is processed independently through the machine learning model, allowing parallel computation and improving productivity while managing complexity through modular processing of image segments rather than analyzing the entire high-resolution image at once.
2Measurement precision
If multiple image scales are used to capture subtle tissue morphology differences, then measurement precision is improved, but processing time increases
Solution Approach 1:
The system performs preliminary resampling of the histological image to generate multiple patches at different spatial resolutions before the main analysis. This preliminary action creates a hierarchy of image representations that enables the machine learning model to efficiently detect subtle morphological differences across scales without processing all scales simultaneously, thereby reducing overall processing time while maintaining measurement precision.
3Reliability
If expert medical opinion is used to predict clinical information, then reliability is maintained, but productivity decreases
Solution Approach 1:
The machine learning model is trained on histological images and gene alteration data to perform self-service in predicting clinical information. The model automatically learns to identify subtle tissue morphology patterns and immunohistochemical staining characteristics that would require expert medical knowledge, enabling rapid and reliable predictions without human intervention while maintaining the reliability needed for clinical decision-making.
Data Source
AI summary
Methods for multiple instance learning of tissue sample images are described. The methods may comprise, for example, receiving a whole slide image from a needle core biopsy sample from a subject; identifying a tissue region in the whole slide image; selecting a set of image patches from the identified tissue region; resampling the set of image patches at a plurality of image scales to generate a plurality of resampled image patches; generating image representations for the plurality of resampled image patches; extracting feature vectors based on the image representations; providing the feature vectors as input to a trained machine learning model configured to predict a gene alteration state; and outputting the predicted gene alteration state for the needle core biopsy sample for the subject.