Multimodal Mapping Framework for Medical Image Annotation
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Solution Overview
Problem
Deep learning models in medical imaging require large amounts of annotated data, suffer from asynchronous model development phases, and lack explainability, making them inefficient and disjointed in data curation and model deployment.
Innovation Solution
An iterative framework for learning multimodal mappings that combines data annotation and model development, utilizing bi-directional mappings between image and non-image features, such as text data, to enhance model performance and explainability in medical image inferencing tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are used for medical image processing, then performance in tasks like organ segmentation and anomaly detection is improved, but the requirement for large-scale annotated data increases
Solution Approach 1:
The system performs preliminary actions by using the trained deep learning model to generate pseudo-labels for unannotated images before actual annotation occurs. This pre-labeling process prepares the data in advance, reducing the burden of manual annotation while maintaining model performance through iterative refinement of labels and model training
Solution Approach 2:
The system creates copies of annotated data by generating pseudo-labels for unannotated images using the trained model. These pseudo-labeled images serve as synthetic training data copies that expand the available annotated dataset without requiring additional manual annotation effort, thereby reducing the quantity requirement for large-scale annotated data
2Measurement precision
If deep learning models are used for medical image processing, then task performance is improved, but the development process becomes asynchronous and disjointed across different phases
Solution Approach 1:
The system merges previously separate development phases (data annotation, model training, and evaluation) into an integrated iterative framework. The annotation component, machine learning component, and model training occur in a unified loop where pseudo-labels generated by the model feed back into the annotation process, eliminating the asynchronous disjointed nature of traditional development workflows
Solution Approach 2:
The system implements feedback mechanisms where the trained model's predictions are used to generate pseudo-labels that are fed back into the annotation process. This feedback loop allows continuous refinement of both the annotation quality and model performance, transforming the asynchronous development process into a cohesive iterative system where each phase informs and improves the others
3Measurement precision
If deep learning models are used for medical image processing, then diagnostic accuracy is improved, but explainability of model decisions deteriorates
Solution Approach 1:
The system introduces an intermediary component that bridges the gap between the black-box deep learning model and the clinician. The annotation component acts as a mediator by generating human-interpretable pseudo-labels and structured annotations that explain the model's decisions in clinically relevant terms, preserving explainability while maintaining diagnostic accuracy
Data Source
AI summary
An iterative framework for learning multimodal mappings tailored to medical image inferencing tasks is provided. In an example, a computer-implemented method can comprise receiving multimodal annotation data for medical images, the multimodal annotation data comprising non-image annotation data and image annotation data, and employing one or more machine learning (ML) processes to learn bi-directional mappings between non-image features included in the non-image annotation data and image features associated with the medical images and the image annotation data. The method further comprises generating, as a result of the one or more ML processes, a model configured to: infer one or more of the non-image features associated with new medical images given the new medical images, and/or infer one or more of the image features associated with the new medical images given the new medical images and non-image input corresponding to at least some of the non-image annotation data.