Pseudo-Stained Image Classification Model for Pathology
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Solution Overview
Problem
The reliance on pathologists to visually analyze histological stained tissue samples for cell classification is time-consuming and prone to errors, leading to pathologist fatigue and potential misdiagnoses, while existing automated technologies are expensive and complex, limiting their widespread use in medical facilities.
Innovation Solution
A classification model trained with pseudo-stained images generated from immunofluorescent images is used to automatically classify cells in histological stained images, allowing for the deployment of this technology in medical facilities to classify cells without the need for sophisticated equipment or extensive pathologist involvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pathologists manually analyze histological stained tissue samples, then diagnostic accuracy can be maintained, but the process is time-consuming and leads to pathologist fatigue
Solution Approach 1:
The patent creates pseudo-stained images that replicate the visual appearance of histological stained images from immunofluorescence data. These synthetic copies allow training of classification models on abundant immunofluorescence data while enabling deployment on standard histological images, thus automating the time-consuming manual analysis while preserving diagnostic accuracy
Solution Approach 2:
The patent transforms the classification approach by changing the training data parameters - instead of training directly on histological images with manual labels, it trains on immunofluorescence images with automatic labels, then applies the model to histological images. This parameter change enables automation while maintaining accuracy
2Productivity
If automated cell classification technology is implemented, then pathologist workload is reduced, but the technology is expensive and complex
Solution Approach 1:
The patent introduces pseudo-stained images as an intermediary that bridges immunofluorescence data and histological images. This intermediary allows the classification model to be trained on rich immunofluorescence data while deploying on simple, widely-available histological images, avoiding the need for complex immunofluorescence equipment in deployment settings
Solution Approach 2:
The trained classification model achieves universal applicability - it can classify cells in both immunofluorescence images and standard histological images. This multi-functionality allows the system to be deployed in facilities with standard equipment while benefiting from training on more sophisticated data
3Measurement precision
If immunofluorescence images are used for training, then classification accuracy improves, but the equipment is expensive and not widely available
Solution Approach 1:
The patent creates pseudo-stained images that copy the essential features of immunofluorescence images in a format suitable for training. These synthetic images preserve the cell classification information while being generated from data that can be obtained and processed in standard facilities
Solution Approach 2:
The patent extracts the essential cell classification information from immunofluorescence images and encodes it into pseudo-stained images. This extraction separates the critical diagnostic features from the expensive imaging modality, allowing training on high-quality data while deployment on accessible equipment
Data Source
AI summary
Methods and systems are provided for automatically classifying cells in a histological stained image. In an example, a method includes automatically classifying a plurality of cells in an image of a biological sample stained with a histological stain using a classification model, the classification model trained with a plurality of automatically-classified pseudo-stained images each generated from a respective immunofluorescent image.