Neural Network Cell Detection Error Recognition
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Solution Overview
Problem
Current methods for computationally detecting cells in tissues are error-prone, leading to under- or over-detection, and lack automated steps for recognizing or predicting errors, which is time-consuming and laborious for scientists to correct manually.
Innovation Solution
A system and method that uses a computational cell detection module with a single-layered neural network to recognize and predict errors in cell detection based on geometric and morphological properties, implemented in a GUI to flag errors and display them alongside tissue- and subcellular-scale dynamics data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If watershed algorithm is used for cell detection, then cell membranes can be detected and tracked, but detection accuracy deteriorates due to under- or over-detection errors
Solution Approach 1:
The patent implements a feedback mechanism where detected cell errors are fed back into the system to train and refine the detection algorithm. By continuously learning from detected errors and updating the computational model, the system progressively improves its detection accuracy and reliability without requiring complete manual re-detection of all cells.
Solution Approach 2:
The system performs self-correction by automatically identifying its own detection errors through the learned neural network model and correcting them autonomously. This self-service capability allows the system to improve its performance without external intervention for each individual correction, significantly reducing the manual labor required while maintaining high accuracy.
2Reliability
If manual error recognition is performed, then detection errors can be corrected, but analytical time is wasted due to tedious and laborious tasks
Solution Approach 1:
The system automatically identifies and corrects detection errors using a learned neural network model, eliminating the need for manual error recognition. The system serves itself by autonomously detecting its own mistakes and correcting them, thereby recovering the significant analytical time that would otherwise be spent on manual review and correction.
Solution Approach 2:
The system performs preliminary error detection and correction before final analysis is completed. By proactively identifying and correcting errors in advance, the system prevents time-consuming manual intervention later in the analytical process, ensuring that corrections are made efficiently at the earliest opportunity.
3Productivity
If automated error recognition is implemented, then analytical time is saved, but device complexity increases due to additional computational steps
Solution Approach 1:
The patent merges the error detection and correction functions into an integrated computational framework that works alongside existing cell detection algorithms. By combining these functions rather than implementing separate independent systems, the patent reduces overall computational complexity while maintaining automated error recognition capabilities that significantly improve productivity.
Solution Approach 2:
The system changes computational parameters by using a learned neural network model that processes geometric and morphological features to predict errors. This parameter-based approach allows automated error recognition through mathematical transformations and pattern recognition rather than complex rule-based systems, improving efficiency while controlling computational complexity.
4Loss of time
If single-layered neural network is used for error recognition, then computational time is reduced, but model accuracy may be insufficient for complex error patterns
Solution Approach 1:
The patent optimizes the neural network by carefully selecting and transforming input parameters representing geometric and morphological cell features. By effectively engineering these parameters and using appropriate weighting, the single-layered network achieves sufficient accuracy for error detection without requiring deeper, more computationally intensive architectures, thus balancing speed and precision.
Solution Approach 2:
The system applies different levels of computational complexity to different aspects of error detection. The single-layered neural network handles the most common and straightforward error patterns efficiently, while more complex error types can be addressed through targeted analysis of specific geometric and morphological parameters, achieving good overall accuracy without uniform high complexity throughout the entire system.
Data Source
AI summary
Exemplary embodiments of a system and method that can learn to predict and recognize errors in the computational detection of cells within tissues from one or more images based on one or more input measurements and display them on a graphical user interface alongside tissue-, cell- and subcellular-scale dynamics data, comprising: a computing device comprising a computational cell detection module configured to take input data and detect cells within tissues and calculate the cells' geometric and morphological properties, the computational cell detection module configured to implement a single-layered neural network to recognize erroneously detected cells within tissues, an error recognition module comprising sigmoid configured to take a weighted sum of measurements of the geometric and morphological properties and return a value between 0 and 1, computational cell detection module configured to recognize errors such as under-detection errors within tissues and record and feed errors to a database.