Microscope Video Stream Analysis for Tiny Lesion Detection
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Solution Overview
Problem
Existing medical image analysis methods lack accuracy in classification results due to insufficient utilization of temporal information in image video streams, leading to potential missed detection of tiny lesion regions during rapid observation.
Innovation Solution
An image analysis method that extracts video frames from a medical image video stream, performs single-frame feature extraction, and conducts classification mapping based on a video stream feature sequence using a neural network model, incorporating operation and patient information to enhance the accuracy of diagnostic classification results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single-frame image analysis is used, then processing speed is fast, but classification accuracy is low and tiny lesion regions are missed
Solution Approach 1:
The patent transitions from analyzing single static images to processing video streams with temporal dimension. By extracting features from multiple frames over time and incorporating operation sequences, the system adds a temporal dimension to the analysis, enabling detection of tiny lesion regions that may be missed in single-frame analysis while maintaining manageable processing complexity through efficient feature extraction and fusion mechanisms.
2Measurement precision
If manual annotation is used for training, then data quality is high, but labor cost and time consumption are high
Solution Approach 1:
The patent implements self-service through automatic operation sequence extraction from video streams. The system automatically identifies and extracts meaningful operation sequences without requiring manual annotation, using the inherent temporal and operational patterns in the video data. This self-service mechanism significantly reduces labor cost and time consumption while maintaining high data quality for training the classification model.
3Measurement precision
If video stream processing is used, then temporal information is utilized, but processing complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the video stream processing into distinct modular components: frame extraction module, feature extraction module, operation sequence extraction module, and classification module. Each module handles a specific aspect of the processing pipeline, making the overall complex task manageable and efficient. This segmentation allows the system to utilize temporal information from video streams while keeping processing complexity controlled through specialized sub-components.
Data Source
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AI summary
An image analysis method, a microscope video stream processing method, and a related apparatus. The method comprises: extracting a video frame of an image video stream, the medical image video stream comprising two or more frames of target object-based video frames (S202); extracting a single frame image feature of the video frame, and mapping same into a single frame diagnosis classification result (S204); and performing, on the basis of a video stream feature sequence, classification mapping to obtain a target classification result (S206), the video stream feature sequence comprising the single frame image feature of the video frames and the single frame classification result. Feature extraction and classification mapping are performed on the basis of the image video stream comprising two or more video frames, and information of the image video stream in a time dimension is reasonably utilized to obtain the target classification result; therefore, the accuracy of the classification result is improved, thereby providing an accurate basis for object classification.