Biomarker Detection From Whole-Slide and Cell-Level Pathology Embeddings
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Solution Overview
Problem
Current methods for cancer biomarker identification, particularly in histological analysis of H&E-stained pathology slides, are insufficient for accurately classifying tumors due to morphological differences beyond human detection limits, and existing machine learning approaches fail to capture useful information from individual cells in whole-slide images.
Innovation Solution
A biomarker detection system that extracts both tile-level and cell-level embeddings from whole-slide images, combining these embeddings to capture histology and cytology features, using machine learning models like ResNet and transformers to generate accurate predictions about biomarker presence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual examination of H&E-stained slides is used for cancer diagnosis, then the method is widely available and easy to operate, but the detection precision is insufficient for classifying some tumors because morphological differences are beyond human detection limits
Solution Approach 1:
The patent segments the whole-slide image into multiple tiles, and further segments each tile into individual cell instances. This hierarchical segmentation allows the system to analyze morphological features at both tissue level (tiles) and cellular level (individual cells), thereby improving detection precision for tumor classification while managing computational complexity through divide-and-conquer approach
Solution Approach 2:
The patent transitions from traditional 2D visual examination to multi-dimensional analysis by extracting features across multiple levels (slide-level, tile-level, and cell-level embeddings). This dimensional expansion enables capture of morphological patterns that are invisible to human observers, improving classification accuracy without requiring complex additional hardware
2Reliability
If machine learning models analyze only tile-level features from whole-slide images, then the computational complexity is reduced, but the model performance deteriorates because useful information from individual cells is not captured
Solution Approach 1:
The patent segments each tile into individual cell instances and generates separate embeddings for each cell. This cell-level segmentation captures morphological information that would be lost in aggregate tile-level analysis, thereby improving model reliability for biomarker detection while managing complexity through efficient embedding aggregation strategies
Solution Approach 2:
The patent merges tile-level embeddings and cell-level embeddings into a unified slide-level representation. This combination integrates both contextual tissue-level patterns and detailed cellular-level features, improving model performance by leveraging complementary information from multiple analysis levels without requiring completely separate analysis systems
3Measurement precision
If molecular tests are used for biomarker identification, then the classification accuracy is improved, but the cost and time consumption increase significantly
Solution Approach 1:
The patent creates a digital copy (whole-slide image) of the physical tissue sample and performs virtual analysis through machine learning models. This digital surrogate allows for rapid, automated analysis that mimics the accuracy of molecular tests but executes much faster, reducing time consumption while maintaining classification accuracy through advanced image analysis algorithms
Data Source
AI summary
A method of detecting a biomarker by a detection system based on machine learning includes identifying, by the detection system, a plurality of tiles corresponding to whole-slide image data of a tissue sample; generating, by the detection system, tile-level embeddings data based on the plurality of tiles; generating, by the detection system, cell-level embeddings data based on the plurality of tiles; and generating, by the detection system, a slide-level prediction based on the tile-level embeddings data and the cell-level embeddings data, the slide-level prediction indicating presence or absence of the biomarker in the tissue sample.