Hierarchical Deep Learning Models for Radiologist-Specific Diagnosis
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Solution Overview
Problem
Diagnostic preferences among radiologists vary significantly, leading to subjective and inconsistent medical imaging diagnoses, which existing technologies fail to accurately accommodate and standardize.
Innovation Solution
An ensemble of continuously-learning deep neural networks is implemented as digital twins of radiologists, customized to match individual and institutional diagnostic methods, allowing for personalized and objective computer-aided diagnoses while preventing overfitting through ensemble-wise updates and comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a deep neural network is trained on a plurality of diagnoses provided by a radiologist to generate personalized computer-aided diagnoses, then diagnostic accuracy and personalization are improved, but overfitting occurs reducing generalization capability
Solution Approach 1:
The model training is segmented into multiple levels: individual radiologist models are first trained on each radiologist's diagnoses, then institution models are trained by aggregating multiple radiologist models, and finally geographic models are trained by combining multiple institution models. This hierarchical segmentation prevents overfitting at any single level while maintaining diagnostic accuracy.
Solution Approach 2:
Multiple radiologist models are merged to create institution models, and multiple institution models are merged to create geographic models. This merging process combines diverse diagnostic perspectives and prevents any single model from overfitting to a specific radiologist's preferences, thereby improving generalization capability.
2Adaptability or versatility
If deep learning models are continuously updated with new medical images and diagnoses, then model performance and adaptability are improved, but computational resources and training time are consumed
Solution Approach 1:
The system performs preliminary actions by pre-training models at multiple hierarchical levels (radiologist, institution, geographic) before deployment. This preliminary training establishes a robust foundation that reduces the need for extensive retraining when new data arrives, thereby reducing computational resource consumption and training time for continuous updates.
3Stability of the object's composition
If multiple levels of deep learning models (radiologist, institution, geographic) are trained and maintained, then diagnostic consistency and objectivity are improved, but system complexity increases
Solution Approach 1:
The complex system is segmented into three distinct hierarchical levels: radiologist models, institution models, and geographic models. Each level serves a specific function and can be independently trained and updated, which manages complexity while maintaining diagnostic consistency across different levels.
Solution Approach 2:
The multi-level model ensemble serves multiple functions: it provides personalized diagnoses (radiologist level), ensures institutional standards (institution level), and maintains geographic consistency (geographic level). This universal approach handles diverse diagnostic requirements through a unified hierarchical framework.
Data Source
AI summary
Various methods and systems are provided for computer-aided diagnosis. In one embodiment, a method comprises acquiring, with an imaging system, a medical image of a subject, generating, with a radiologist model associated with a radiologist of an institution, a computer-aided diagnosis for the medical image, the radiologist model comprising a deep neural network trained on a plurality of diagnoses provided by the radiologist, displaying, to the radiologist via a display device, the medical image and the computer-aided diagnosis, and selectively updating, based on the medical image, one or more of the radiologist model, an institution model associated with the institution, and a geographic model associated with a geographic area containing the institution. In this way, a radiologist may be assisted by a deep neural network model configured as a digital twin of the radiologist.