Biomedical Image Analysis With Attention-Guided Abnormal Feature Detection
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Solution Overview
Problem
Medical professionals face challenges in detecting medically relevant features from large quantities of unlabeled biomedical data, particularly in formats like electrocardiogram (ECG) data, which often require manual analysis.
Innovation Solution
An apparatus and method using a convolutional neural network (CNN) and machine-learning model with an attention layer to extract and highlight abnormal biomedical features within images, facilitated by an image capture device, processor, and display device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis by specialists is used to detect medically relevant features, then detection accuracy is improved, but productivity deteriorates due to time-consuming analysis
Solution Approach 1:
The patent replaces manual mechanical analysis by specialists with an automated machine-learning system. The CNN-based feature extractor and attention mechanism automatically process biomedical images to identify medically relevant features, eliminating the need for manual specialist analysis while maintaining detection accuracy and significantly improving productivity.
Solution Approach 2:
The patent introduces an attention mechanism as an intermediary between the feature extraction process and final detection. This attention layer selectively weights different biomedical features, allowing the system to focus on the most medically relevant patterns while filtering out noise, thereby achieving high accuracy automatically without manual intervention.
2Reliability
If a large quantity of unlabeled clinical data is analyzed, then detection completeness is improved, but device complexity deteriorates due to data processing requirements
Solution Approach 1:
The patent segments the complex task of analyzing large unlabeled datasets into distinct processing stages: initial feature extraction using CNN, followed by attention-based feature weighting and selection. This segmentation allows the system to handle large volumes of data systematically, processing features in manageable groups rather than attempting to analyze all raw data simultaneously, thus reducing computational complexity while maintaining detection completeness.
Solution Approach 2:
The patent extracts only the most relevant biomedical features from large quantities of unlabeled data using the attention mechanism. Instead of processing all extracted features equally, the attention layer identifies and extracts only those features with high diagnostic value, effectively filtering out redundant information and reducing the complexity of subsequent analysis while preserving detection completeness.
3Productivity
If automated machine-learning analysis is used to process biomedical data, then productivity is improved, but measurement precision deteriorates due to potential loss of expert judgment
Solution Approach 1:
The patent replaces manual specialist analysis with an automated machine-learning system that processes biomedical data at high speed. The CNN-based architecture automatically learns diagnostic patterns from training data, enabling rapid analysis of large datasets while maintaining high detection accuracy through sophisticated feature extraction and attention mechanisms that simulate expert judgment capabilities.
Solution Approach 2:
The patent transforms the analysis process by changing key parameters: instead of relying on human expert time and judgment, the system uses automated computational parameters including convolutional filtering, attention weights, and probabilistic classification. These parameter changes enable high-speed processing while maintaining or exceeding the accuracy of manual analysis through optimized algorithmic decision-making.
Data Source
AI summary
Apparatus for identification of abnormal biomedical features within images of biomedical data and methods used therein are described. The apparatus includes an image capture device, a processor connected to the image capture device, a memory connected to the processor, and a display device connected to the processor. The image capture device is configured to capture an image of biomedical data. The memory contains instructions configuring the processor to receive the image, extract a plurality of biomedical features from the biomedical data, receive repository data from a medical repository as a function of the plurality of biomedical features, generate at least a distance metric as a function of the plurality of biomedical features and the repository data, and highlight at least a biomedical feature within the image as a function of the at least a distance metric.