Probabilistic Biomarker Identification for Consistent Cell Phenotyping
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Solution Overview
Problem
Conventional histological analysis techniques for identifying cellular phenotypes in images are prone to inter- and intra-pathologist variability, and existing machine learning solutions rely on unreliable expert annotations, leading to inaccuracies in disease diagnosis and treatment response predictions.
Innovation Solution
A machine learning-based approach that generates probabilistic outputs for biomarker identification and phenotype determination, using a biomarker identification model to provide precise quantification of uncertainty in cell associations with biomarkers, and identifying cell subsets based on probabilistic outputs to enhance accuracy in phenotype identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional histological analysis techniques are used for identifying cellular phenotypes, then expert knowledge can be applied, but inter- and intra-pathologist variability leads to measurement inaccuracies
Solution Approach 1:
The patent replaces the mechanical/manual histological analysis system performed by pathologists with an automated machine learning-based image analysis system. This substitution eliminates human variability in phenotype identification while maintaining the ability to extract meaningful biological information from histological images through computational algorithms.
Solution Approach 2:
The machine learning model is trained to autonomously identify cellular phenotypes and biomarker associations without requiring continuous human intervention or expert annotation during operation. The system self-calibrates through probabilistic outputs and can independently determine cell classifications, reducing reliance on pathologist expertise for each analysis.
2Measurement precision
If existing machine learning solutions rely on expert annotations for training, then labeled data can be obtained, but annotation unreliability propagates to model inaccuracies
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model generates probabilistic outputs that indicate its confidence level in each prediction. These probabilistic signals serve as feedback to identify uncertain cases that may require re-examination or additional training data, allowing the system to self-correct and improve without propagating annotation errors.
Solution Approach 2:
Instead of relying on a single binary annotation label, the system uses probabilistic outputs that provide a range of possible classifications with associated confidence levels. This partial action approach allows the model to express uncertainty and partially classify cells when annotation confidence is low, preventing error propagation while still providing useful diagnostic information.
3Ease of operation
If deterministic classification is used for cell phenotype identification, then clear categories can be assigned, but uncertainty in biomarker associations cannot be quantified
Solution Approach 1:
The patent adds a probabilistic dimension to the traditional deterministic classification output. Instead of simply assigning cells to discrete phenotype categories, the system outputs probability distributions across multiple possible classifications, maintaining the simplicity of category assignment while adding the dimension of uncertainty quantification through probabilistic values.
Data Source
AI summary
A method may include extracting a plurality of features for each cell depicted in an image. A biomarker identification model may be applied to determine, based on the features associated with each cell, whether the cell is associated with various biomarkers. A set of probabilities for each cell in the population of cells may be determined based on an output of the biomarker identification model. The set of probabilities may include, for each biomarker, a probability of a corresponding cell being associated with the biomarker. One or more subsets of cells, each of which corresponding to a different cellular phenotype, may be identified based on the set of probabilities associated with each cell. A feature set associated with each subset of cells may be identified as being indicative of a probability of a cell being associated with a corresponding phenotype. Related systems and computer program products are also provided.