Cytological Grading via Tile-Based Machine Learning
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Solution Overview
Problem
Current methods for cytologically grading mast cell tumors in dogs are time-consuming and prone to inter-observer variability, necessitating a more efficient and accurate approach.
Innovation Solution
A computer-implemented method and system using machine learning models to analyze and grade cytological samples by parsing digital microscopic images into tiles, identifying cytological features, and determining statistics for grading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a pathologist examines each cell in detail in a digital microscopic image, then measurement precision of cytological grading is improved, but loss of time increases significantly
Solution Approach 1:
The patent divides the large digital microscopic image into multiple smaller tiles (e.g., 224x224 pixels each) that can be processed independently by machine learning models. This segmentation allows the system to analyze the entire slide comprehensively while maintaining processing efficiency, resolving the contradiction between thorough examination and time consumption.
Solution Approach 2:
The patent replaces the manual mechanical process of pathologist examination with an automated machine learning system. The ML models automatically detect, count, and measure cytological features such as nuclei size, shape, and mitotic figures, eliminating the time-consuming manual review while maintaining grading accuracy through trained algorithms.
2Measurement precision
If manual cytological grading is performed by pathologists, then measurement precision can be maintained, but inter-observer variability increases
Solution Approach 1:
The patent applies machine learning models that provide homogeneous, consistent grading across all samples. The algorithms use standardized criteria and trained parameters to ensure uniform evaluation, eliminating the variability inherent in manual grading by different pathologists. Each sample is evaluated using the same objective standards, ensuring reliability and consistency.
3Measurement precision
If the entire digital microscopic image is analyzed manually, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the large image into smaller manageable tiles that can be processed by individual machine learning models. This division reduces the computational complexity required for each processing step while maintaining comprehensive coverage of the entire slide, making the system more manageable and scalable.
Solution Approach 2:
The patent introduces an intermediary processing layer where machine learning models act as mediators between the raw image data and the final grading output. These models automatically extract features, count cells, and determine grades, simplifying the overall system architecture by automating the complex analysis process that would otherwise require sophisticated manual examination protocols.
Data Source
AI summary
A computer-implemented method for cytological grading may include receiving first image data representing a biological sample, and parsing the received first image data into a plurality of tiles. Each of the plurality of tiles may represent a respective portion of the received first image data. The method may also include identifying, using a first trained machine learning model, at least one cytological feature for each of the plurality of tiles. The method may include determining, using a second trained machine learning model, at least one statistic based on the identified at least one cytological feature for each of the plurality of tiles, and outputting the determined at least one statistic.