Machine Learning Cell Classification for Macrophage–Stromal Separation
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Solution Overview
Problem
Existing cell classification models struggle to accurately differentiate between macrophages and stromal cells, particularly non-pigmented stromal macrophages, which are often confounded with fibroblasts, leading to poor performance in identifying macrophages in biological samples, and this ambiguity affects the prediction of treatment response in cancer patients.
Innovation Solution
A machine learning-based cell classification model is trained to recognize macrophages and stromal cells as separate classes, using high-confidence expert annotations for macrophages and low-confidence annotations for stromal cells, improving the model's accuracy in distinguishing between foamy, alveolar, and pigmented stromal macrophages and fibroblasts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing cell classification models are used to differentiate between macrophages and stromal cells, then the model structure is simple, but the classification accuracy is poor due to confusion between macrophages and fibroblasts
Solution Approach 1:
The patent segments the cell classification task into multiple specialized models, each trained to differentiate between specific cell types (e.g., macrophages vs. fibroblasts, tumor cells vs. stromal cells). This segmentation allows each model to focus on specific morphological features and staining patterns, improving overall classification accuracy without requiring a single overly complex model to handle all cell types.
Solution Approach 2:
The patent applies local quality by training different classification models for different tissue regions and cell types, with each model optimized for specific local characteristics. The system uses region-specific staining patterns and morphological features to improve classification accuracy in different areas of the tissue sample.
2Reliability
If the model differentiates between macrophages and stromal cells with high accuracy, then the treatment response prediction improves, but the computational time and processing complexity increase
Solution Approach 1:
The patent performs preliminary actions by pre-training multiple cell classification models on large datasets of annotated tissue images before actual analysis. These pre-trained models capture essential morphological features and staining patterns in advance, allowing rapid inference during treatment response assessment without requiring time-consuming real-time training.
Solution Approach 2:
The patent creates copies of tissue samples through digital imaging and uses these digital copies for analysis. The system generates synthetic training data by processing multiple images of the same tissue region, allowing the models to learn from replicated data without repeatedly analyzing the same physical samples, thus reducing processing time.
Data Source
AI summary
A method may include applying a cell classification model to identify, based at least on an image of a biological sample, one or more cell types present in the biological sample. The cell classification model may be trained to differentiate between a plurality of cell types including a first cell type whose likelihood of being a macrophage satisfies a threshold and a second cell type whose likelihood of being the macrophage fails to satisfy the threshold. A composition profile for the biological sample may be generated based on the one or more cell types identified in the biological sample. At least one of a disease diagnosis, a disease progress, a disease burden, and a treatment response for a patient associated with the biological sample may be determined based on the composition profile of the biological sample. Related systems and computer program products are also provided.