Automated Pathological Image Generation Using Feature Extraction
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Solution Overview
Problem
Current techniques for generating pathological images for diagnosis require complex operations, making it time-consuming to obtain the necessary images for medical professionals.
Innovation Solution
An image processing device that extracts feature amounts from medical images using statistical learning to detect observation positions and determine the order of observation, allowing for the automated generation of diagnostic moving images that highlight critical areas for attention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual operation inputs are performed to generate pathological images for diagnosis, then the images can be obtained, but the process becomes time-consuming and complex
Solution Approach 1:
The system automatically generates pathological images for diagnosis by itself without requiring manual operation inputs from users. The image processing device autonomously performs feature extraction, observation position detection, and image generation based on input medical images, eliminating the need for users to perform complex manual operations while maintaining diagnostic quality
Solution Approach 2:
The patent replaces manual mechanical operation inputs with automated computational processing. Instead of users manually controlling microscope operations, the system uses computer-based feature amount extraction, statistical learning models, and automated image generation algorithms to produce pathological images, substituting human manual operations with automated digital processing
2Measurement precision
If multiple operation inputs are performed manually for each new microscopic image, then accurate pathological images can be obtained, but the time required increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-learning statistical patterns from existing medical images and expert annotations to create observation position dictionaries and feature extraction models. When new images are input, the system leverages these pre-learned knowledge to quickly and accurately identify observation positions and generate diagnostic images without requiring time-consuming manual analysis for each new image
Solution Approach 2:
The system incorporates feedback mechanisms where the generated pathological images and their accuracy are fed back into the learning process. The statistical learning models continuously improve by learning from the feedback between automated generation results and ground truth annotations, enabling increasing accuracy over time without proportionally increasing manual intervention time
Data Source
AI summary
An image processing device includes: a first feature amount extraction unit configured to extract a first feature amount from an image; a position detection unit configured to detect observation positions from the image based on a position detection dictionary, and the first feature amount extracted from the image; a second feature amount extraction unit configured to extract a second feature amount from the observation position; an observation-order determining unit configured to determine the order of observing the observation positions based on an order generation dictionary, and respective second feature amounts of the observation positions; and an image generation unit configured to generate observation images for displaying the observation positions in the observation order based on the image, the detected observation positions and the determined observation order.