Microscopic Image Analysis for Classifier Training
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Solution Overview
Problem
Conventional methods for training convolutional neural networks for nucleus detection in histological samples are time-consuming and cumbersome, requiring large annotated datasets and being heavily dependent on user interpretation, leading to inconsistent results.
Innovation Solution
A system and method for efficient analysis of microscopic image data using a data processing system that generates pixel classification data, groups pixels into probabilistic groups, and calculates group classification data, allowing for interactive generation of annotated datasets to train classifiers with reduced user dependency and increased efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional nucleus detection algorithms are used for training convolutional neural networks, then recognition accuracy can be achieved, but the process requires large annotated datasets (10,000-100,000 annotations) and is time-consuming
Solution Approach 1:
The patent segments the image analysis process into pixel-level classification and group-level classification. By first classifying individual pixels and then grouping them into candidate regions, the system reduces the annotation burden while maintaining accuracy. This segmentation allows the convolutional neural network to learn from fewer, more strategically selected annotations rather than requiring exhaustive pixel-level labeling of entire datasets.
Solution Approach 2:
The patent performs preliminary pixel classification and grouping before final nucleus detection. By pre-processing the image data to identify candidate pixel groups with high probability of containing nuclei, the system prepares the data in advance for more efficient training. This preliminary action reduces the amount of data that requires full annotation, thereby reducing time loss while preserving recognition accuracy.
2Quantity of substance
If manual annotation by users is used to prepare training datasets, then annotated data can be generated, but the results strongly depend on the user and are inconsistent
Solution Approach 1:
The patent enables the system to perform self-service through automated pixel classification and group formation. The convolutional neural network automatically classifies pixels and identifies candidate regions without requiring manual user annotation. This self-service approach eliminates user dependency and ensures consistent, reproducible results across different runs and users while still generating sufficient annotated data for training.
Solution Approach 2:
The patent replaces the mechanical manual annotation process with an automated computational system. Instead of relying on human users to manually label nuclei, the system uses pixel classification algorithms and convolutional neural networks to automatically identify and annotate candidate regions. This substitution eliminates the inconsistency inherent in manual processes while maintaining the necessary quantity of annotated data.
3Measurement precision
If pixel-level classification is performed for all pixels, then detailed classification data is obtained, but the processing complexity and time increase significantly
Solution Approach 1:
The patent segments the processing into two stages: pixel-level classification to identify candidate regions, and group-level classification for final nucleus detection. This segmentation reduces overall processing complexity by focusing detailed analysis only on regions with high probability of containing nuclei, rather than processing all pixels with equal detail. The system maintains pixel-level accuracy where needed while reducing complexity in low-probability regions.
Solution Approach 2:
The patent applies different levels of processing quality to different regions of the image. High-probability regions (likely to contain nuclei) receive full pixel-level classification attention, while low-probability regions receive minimal processing. This local quality approach maintains measurement precision in critical areas while reducing overall processing complexity and computational resources required.
4Measurement precision
If large annotated datasets are used for training, then classifier accuracy improves, but the data preparation process is cumbersome and inefficient
Solution Approach 1:
The patent performs preliminary pixel classification and group formation before final classifier training. By pre-identifying candidate pixel groups that are likely to contain nuclei, the system prepares a focused subset of data for annotation and training. This preliminary action enables the generation of sufficient training data with high classifier accuracy while dramatically improving data preparation efficiency by avoiding the need to process and annotate entire large datasets.
Solution Approach 2:
The system performs self-service by automatically generating candidate pixel groups for training data preparation. The convolutional neural network and pixel classification algorithms autonomously identify regions suitable for annotation, eliminating the need for manual curation of large datasets. This self-service capability maintains classifier accuracy while making the data preparation process efficient and scalable.
Data Source
AI summary
Disclosed is a system for analysis of microscopic image data which includes a data processing system. Pixel classification data for each of a plurality of pixels of the microscopic image data are read. The pixel classification data include for each of the pixels of the microscopic image data, binary or probabilistic classification data for classifying the pixel of the microscopic image data into one or more object classes of pre-defined objects which are shown by the image. At least a portion of the pixels of the microscopic image data are grouped to form one or more pixels groups. For each of the pixel groups, probabilistic group classificati on data are calculated depending on the pixel classification data of the pixels of the respective group. The probabilistic group classification data are indicative of a probability that the group shows at least a portion of an object of the respective object class.