Cell Image Analysis Using Latent Projections for Outlier Screening
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Solution Overview
Problem
Deep learning models for cell image analysis face challenges in reliably handling outlier data, which can result from unobserved cell types or imaging errors, leading to unpredictable model behavior and unreliable predictions.
Innovation Solution
A computer-implemented method that uses a latent variable model constructed from intermediate output values of a deep neural network to determine whether a new cell image is an outlier relative to a training dataset, allowing for the evaluation of prediction reliability by mapping intermediate output values to projected values in a lower-dimensional subspace and calculating distances to identify outliers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep neural networks are used for cell image analysis, then analysis capability is improved, but reliability deteriorates when outlier data is encountered
Solution Approach 1:
The patent applies preliminary action by constructing a latent variable model from training data before actual analysis. This model captures the distribution of normal cell images in advance, enabling the system to evaluate whether new images conform to expected patterns. When an outlier is detected, the system can reject unreliable predictions before they are made, thus maintaining reliability while preserving analysis capability.
2Reliability
If outlier detection is implemented to improve reliability, then prediction reliability is improved, but device complexity increases
Solution Approach 1:
The patent introduces a latent variable model as an intermediary between the deep neural network and the prediction output. This intermediary component evaluates whether input images conform to the distribution learned from training data, acting as a gatekeeper that filters out outliers. While this adds a component to the system, it maintains modularity and can be integrated with existing deep learning pipelines, thereby improving reliability without excessively increasing overall system complexity.
3Measurement precision
If latent variable model is constructed from intermediate output values, then outlier detection accuracy is improved, but loss of information increases due to dimensionality reduction
Solution Approach 1:
The patent extracts intermediate output values from specific layers of the deep neural network to construct the latent variable model. By selecting representative intermediate outputs rather than using all raw input data or final outputs, the system captures essential features for outlier detection while reducing dimensionality. This extraction approach maintains detection accuracy by focusing on discriminative features while minimizing information loss through intelligent selection of latent representations.
Data Source
AI summary
A computer-implemented method for analysis of cell images comprises obtaining a deep neural network and a training dataset, the deep neural network comprising a plurality of hidden layers; obtaining first sets of intermediate output values that are output from at least one of the plurality of hidden layers; constructing a latent variable model using the first sets of intermediate output values, the latent variable model mapping the first sets of intermediate output values to first sets of projected values in a sub-space that has a dimension lower than the sets of the intermediate outputs; obtaining a second set of intermediate output values by inputting a received new cell image to the deep neural network; mapping, using the latent variable model, the second set of intermediate output values to a second set of projected values; and determining whether the received new cell image is an outlier.