Cross-Modality Neural Mapping for Fast Medical Image Annotation
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Solution Overview
Problem
The high cost and regulatory challenges of manually annotating medical images, particularly in medical imaging datasets, hinder the effective utilization of big data in medical imaging, especially when clinical reports are not available or mismatched with images.
Innovation Solution
A neural network-based method that maps medical images to a text vector space using a learned transform, allowing automatic annotation of images based on associated clinical text reports, utilizing a multilayer perceptron (MLP) neural network and pre-trained convolutional networks to generate feature vectors, reducing the need for paired image-text data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation of medical images is performed by experts, then annotation accuracy is improved, but annotation cost and time consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-training convolutional networks on large datasets and pre-processing clinical reports into structured formats before the actual annotation task. This allows the neural network to quickly map new images to text representations without requiring real-time expert intervention, significantly reducing annotation time while maintaining accuracy.
Solution Approach 2:
The patent introduces an intermediary neural network system that mediates between medical images and clinical text reports. The network acts as a bridge, automatically mapping image features to corresponding text descriptions without direct human intervention, thereby reducing both time consumption and expert workload while preserving annotation quality.
2Reliability
If manual annotation by experts is used, then annotation quality is improved, but regulatory challenges and costs increase
Solution Approach 1:
The system implements self-service by enabling automatic annotation through neural networks that process images and generate text descriptions autonomously. This reduces reliance on human experts, thereby minimizing regulatory hurdles associated with expert involvement while maintaining consistent annotation quality through the trained model.
Solution Approach 2:
The patent uses copying by training the neural network on existing paired image-report data, allowing the model to learn and replicate expert annotation patterns. Once trained, the network can generate annotations without direct expert involvement, reducing regulatory complexity while preserving the quality characteristics of expert-annotated data.
3Productivity
If automatic annotation methods are used, then processing speed is improved, but annotation accuracy may deteriorate
Solution Approach 1:
The system performs preliminary training on large datasets of paired images and reports, allowing the neural network to learn accurate mappings before deployment. This pre-training ensures that when the system processes new images at high speed, it maintains annotation accuracy by applying learned patterns rather than making random guesses.
Solution Approach 2:
The patent replaces the mechanical system of manual expert annotation with a neural network-based automatic system. The network processes images through learned feature transformations, achieving both high processing speed and maintained accuracy by substituting human cognitive processes with automated computational patterns.
Data Source
AI summary
A cross-modality neural network transform for semi-automatic medical image annotation is provided. In various embodiments, an input medical image is mapped to a first vector in a text vector space. The first vector corresponds to the features of the medical image. A set of predetermined vectors is searched for a closest one of the predetermined vectors to the first vector. From the closest one of the predetermined vectors, one or more keywords is determined describing the input medical image.