Clinical Cell Visualization Using CNN Bottleneck Embeddings
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Solution Overview
Problem
Existing automatic cell identification systems using machine-learning models fail to provide clinicians with meaningful information about unusual cells due to a lack of training data, making it difficult to make accurate clinical decisions.
Innovation Solution
Implementing a bottleneck layer in a deep learning neural network model to condense data into no more than ten nodes, allowing for a visualization of cell populations in three dimensions, which enhances ergonomic interpretation by clinicians.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a deep learning neural network model is used for automatic cell identification, then classification accuracy is improved, but the output provides insufficient meaningful information for unusual cells
Solution Approach 1:
The patent introduces an intermediary visualization layer between the neural network's bottleneck layer and the clinician. This visualization component translates the abstract activation values into intuitive geometric representations (points in 2D/3D space), allowing clinicians to perceive information about unusual cells without losing the analytical power of the deep learning model.
Solution Approach 2:
The patent transforms the data from the bottleneck layer (no more than ten values) into a visualized output in no more than three dimensions. This dimensionality transformation allows complex high-dimensional data to be perceived intuitively while preserving the essential characteristics of cell populations, including unusual ones.
2Loss of information
If detailed morphological data is provided for all cells, then diagnostic information is improved, but the complexity of data presentation increases
Solution Approach 1:
The patent extracts only the essential morphological information through the bottleneck layer, which condenses cell characteristics into no more than ten activation values. This extraction process removes redundant details while preserving the key features needed for diagnosis, presenting only the most relevant information to the clinician.
Solution Approach 2:
The patent changes the parameter representation from detailed pixel-level or feature-level data to a condensed set of activation values from the bottleneck layer. This parameter transformation reduces data complexity while maintaining diagnostic value, as the bottleneck layer learns to encode essential morphological characteristics in a compressed form.
Data Source
AI summary
A clinical support system comprises a processor and a display component, wherein: the processor is configured to: receive image data, the image data representing an image of a plurality of cells obtained from a human or animal subject, the image data comprising a plurality of subsets of image data, each subset comprising data representing a portion of the image data corresponding to a respective cell of the plurality of cells; apply a trained deep learning neural network model to each subset of the image data, the deep learning neural network model comprising: a plurality of convolutional neural network layers each comprising a plurality of nodes; and a bottleneck layer comprising no more than ten nodes, wherein the processor is configured to apply the trained deep learning neural network model to each subset of the image data by applying the plurality of CNN layers, and subsequently applying the bottleneck layer, each node of the bottleneck layer of the machine-learning model configured to output a respective activation value for that subset of the image data; for each subset of the image data, derive a dataset comprising no more than three values, the values derived from the activation values of the nodes in the bottleneck layer; and generate instructions, which when executed by the display component of a clinical support system, cause the display component of the computer to display a plot in no more than three dimensions of the respective dataset of each subset of the image data. Associated computer-implemented methods, including for training the deep learning neural network model, are provided.