Medical Image Report Model Training With Self-Supervised Labels
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Solution Overview
Problem
Current medical image analysis relies on manual reporting, leading to low efficiency and inaccurate reports, especially for junior doctors.
Innovation Solution
A medical image report generation model using a visual feature extraction network, encoding network, and decoding network, trained with self-supervised learning to automatically generate accurate image reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual medical image reporting is used, then doctors can generate image reports, but the generation efficiency is low and accuracy is poor especially for junior doctors
Solution Approach 1:
The system employs self-supervised learning where the model learns to perform auxiliary tasks (such as image restoration, completion, or classification) without human intervention. This enables the model to automatically improve its feature extraction capabilities and generate more accurate medical image reports, resolving the contradiction between automation efficiency and report reliability.
Solution Approach 2:
The patent changes the training parameters and objectives of the model by introducing self-supervised learning tasks. By modifying the loss functions and training objectives to include auxiliary tasks, the model learns better feature representations that simultaneously improve both generation efficiency and report accuracy.
2Productivity
If automated image report generation is implemented, then generation efficiency improves, but feature extraction and recognition capabilities need enhancement for accuracy
Solution Approach 1:
The system performs preliminary feature extraction and learning through self-supervised tasks before generating the final medical image report. By pre-training the model on auxiliary tasks that require robust feature extraction, the system prepares the model with enhanced capabilities that improve both efficiency and precision in the main reporting task.
Solution Approach 2:
The patent replaces manual feature extraction and analysis with an automated deep learning system. The visual feature extraction network automatically learns and extracts relevant features from medical images, substituting the mechanical manual process with an intelligent system that improves both efficiency and measurement precision through its learned representations.
3Measurement precision
If self-supervised learning is used for training, then feature extraction capability is enhanced, but model training complexity increases
Solution Approach 1:
The model is designed with multi-functionality, serving both as a feature extractor for self-supervised tasks and as a generator for medical image reports. By making the visual feature extraction network universal and applicable to multiple tasks, the system enhances feature extraction capability while managing training complexity through shared representations across different functions.
Data Source
AI summary
A method for training a medical image report generation model that includes a visual feature extraction network, an encoding network, and a decoding network. The method includes: acquiring a sample medical image; extracting visual feature information of on a sample medical image through the visual feature extraction network, to obtain a visual feature sequence; concatenating a self-learning label based on the visual feature sequence, to obtain input information about the encoding network; encoding the input information through the encoding network, to obtain a visual encoding feature vector and an output task result; decoding the visual encoding feature vector through the decoding network, to obtain an output image report; and calculating a loss of the model based on the output image report and the output task result, and adjusting a parameter of the medical image report generation model according to the total loss function value.