Neural Network Feature Alignment for Medical Image-Text Association
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Solution Overview
Problem
Existing methods face challenges in accurately associating medical images with corresponding descriptive sentences due to variations in expression, requiring a large amount of teacher data to construct a model that can accurately derive feature amounts from sentences with different expressions.
Innovation Solution
A learning device that uses two neural networks to derive feature amounts for images and sentences, training them to minimize the distance between corresponding features in a feature space while maximizing the distance between non-corresponding features, allowing for high-accuracy association despite variations in sentence expressions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a model is constructed to accurately derive feature amounts from sentences with various expressions, then the association accuracy between image and sentence is improved, but a large amount of teacher data is required which is difficult to prepare
Solution Approach 1:
The patent segments the sentence processing into two distinct neural networks: one for extracting semantic features from sentences and another for extracting features from images. This segmentation allows each network to specialize in its respective modality, improving feature extraction efficiency without requiring proportionally more training data for the entire system.
Solution Approach 2:
The patent introduces an intermediary attention mechanism that operates between the sentence and image features. This attention mechanism serves as a mediator that dynamically weights and aligns corresponding features between modalities, enabling accurate association without requiring exhaustive training data to learn all possible expression variations directly.
2Adaptability or versatility
If traditional methods are used to extract features from sentences with various expressions, then the model can handle different expression styles, but the training data requirement increases significantly
Solution Approach 1:
The patent divides the feature extraction process into specialized components: a sentence processing network that handles textual variations and an image processing network that handles visual features. This segmentation allows the sentence network to focus on learning expression variations while the image network focuses on visual feature extraction, reducing the overall training data burden.
Solution Approach 2:
The patent employs parameter sharing and freezing techniques where certain parameters in the sentence processing network are shared across different expression types or frozen during training. This reduces the number of trainable parameters, thereby reducing the amount of training data needed while maintaining adaptability to various expression styles.
Data Source
AI summary
A processor derives a first feature amount for an object included in an image by a first neural network, structures a sentence including description of the object included in the image to derive structured information for the sentence, and derives a second feature amount for the sentence from the structured information by a second neural network. The processor trains the first neural network and the second neural network such that, in a feature space to which the first feature amount and the second feature amount belong, a distance between the derived first feature amount and second feature amount is reduced in a case in which the object included in the image and the object described in the sentence correspond to each other.