Focus-Weighted Error Prediction for Microscope Slide Images
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Solution Overview
Problem
In digital pathology, high-resolution microscope slide images often contain out-of-focus regions due to the thin depth of field and uneven tissue samples, leading to inaccurate diagnoses by both human pathologists and machine learning algorithms, and existing auto-focus solutions fail to effectively distinguish between in-focus and out-of-focus blurriness.
Innovation Solution
A method using a deep convolutional neural network to predict classification errors in microscope slide images by characterizing the degree of out-of-focus blur, employing computational Bokeh filtering to simulate and quantify focus-weighted errors, and generating error statistics for different slide scanners to improve diagnosis accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high magnification is used to digitize tissue images, then image resolution is improved, but depth of field becomes extremely thin causing out-of-focus blur
Solution Approach 1:
The patent divides the whole-slide image into multiple smaller patches or tiles, each processed independently through the focus detection neural network. This segmentation allows the system to manage computational complexity while maintaining focus assessment accuracy across the entire gigapixel image, addressing the contradiction between high resolution and focus reliability.
Solution Approach 2:
The patent introduces a focus detection neural network as an intermediary component that predicts focus quality for each patch. This intermediary model bridges the gap between the high-resolution images and the need for reliable focus assessment, enabling the system to handle the thin depth of field at high magnifications effectively.
2Reliability
If local auto-focus method is used while capturing images, then focus quality is improved, but distinguishing between in-focus and out-of-focus blurriness becomes difficult
Solution Approach 1:
The patent replaces traditional mechanical or algorithmic focus detection methods with a deep learning-based neural network approach. This substitution enables the system to accurately distinguish between different types of blurriness (in-focus smooth appearance versus out-of-focus tissue patterns) by training the network to recognize complex visual patterns that are difficult to differentiate using conventional methods.
Solution Approach 2:
The patent changes the approach to focus detection by using a neural network that processes multiple parameters simultaneously (texture, color, structural information) rather than relying on a single focus metric. This multi-parameter approach enables accurate differentiation between in-focus and out-of-focus regions even when traditional focus algorithms fail.
3Measurement precision
If deep convolutional neural network is used to detect cancer cells, then detection accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The patent segments the large gigapixel image into smaller patches that are processed independently by the neural network. This segmentation strategy maintains the high detection accuracy provided by deep learning while significantly reducing the computational burden and processing time compared to analyzing the entire image at once.
Solution Approach 2:
The patent applies the neural network to process only the necessary portions of the image (patches containing tissue of interest) rather than the entire gigapixel image. This partial action approach maintains detection accuracy for critical regions while reducing overall processing time and computational resource consumption.
4Adaptability or versatility
If synthetic out-of-focus blur is applied to master images, then focus quality can be simulated, but image artifacts and noise may be introduced
Solution Approach 1:
The patent uses synthetic out-of-focus blur processing to create training data that simulates real imaging conditions. By intentionally introducing controlled blur during the synthetic image generation process, the system learns to recognize and correct focus-related artifacts, ultimately improving its ability to handle real out-of-focus images without being misled by harmful artifacts.
Data Source
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AI summary
A method is described for generating a prediction of a classification error for a magnified, digital microscope slide image, for example an image of a tissue sample. The image is composed of a multitude of patches or tiles of pixel image data. An out-of-focus degree per patch is computed using a machine learning out-of-focus classifier. Data representing expected classifier error statistics of a machine learning classifier for a plurality of out-of-focus degrees is retrieved. A mapping of the expected classifier error statistics to each of the patches of the digital microscope slide image based on the computed out-of-focus degree per patch is computed, thereby generating a classifier error prediction for each of the patches. The classifier error predictions thus generated are aggregated over all of the patches.