Hierarchical Model Training for Lung Nodule Detection
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Solution Overview
Problem
Current deep learning systems face challenges in capturing high-level relationships between related tasks, such as lung segmentation and lung nodule detection, leading to suboptimal performance in understanding hierarchical relationships like a lung nodule residing within a lung.
Innovation Solution
The approach involves training multiple models in a hierarchical relationship, where one model is trained to perform lung segmentation and another to detect lung nodules, with alternating training processes that impose a spatial hierarchy by multiplying their outputs to enforce the correct anatomical relationship, allowing errors to be backpropagated based on task-specific predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple deep learning models are trained independently for related tasks (lung segmentation and lung nodule detection), then each model can be optimized for its specific task, but the models fail to capture high-level relationships between tasks (e.g., that lung nodules must reside within lungs)
Solution Approach 1:
The patent combines multiple independently trained models into a unified hierarchical framework where models for related tasks (lung segmentation, nodule detection, nodule segmentation) are integrated through shared feature representations and coordinated loss functions. This merging allows the system to capture high-level relationships between tasks while maintaining task-specific optimization capabilities.
Solution Approach 2:
The patent implements a nested hierarchical structure where models operate at different levels of granularity. The lung segmentation model operates at the organ level, the nodule detection model at the lesion level, and the nodule segmentation model at the detailed structure level. Each nested model benefits from the context provided by outer models while contributing specialized features to inner models, capturing spatial and semantic relationships across scales.
2Reliability
If transfer learning, multi-task learning, or alternated training are used to learn relationships between tasks, then some utility is gained in creating robust feature representations, but high-level task relationships (e.g., anatomical constraints) are rarely utilized effectively
Solution Approach 1:
The patent implements feedback mechanisms where the output of one model influences the training of another model through coordinated loss functions. For example, the lung segmentation results provide spatial constraints that feedback into the nodule detection model, and detection results feedback into the nodule segmentation model. This feedback loop ensures anatomical consistency and reinforces high-level relationships during training.
Solution Approach 2:
The patent dynamically adjusts model parameters and loss function weights based on the hierarchical relationships between tasks. The loss function incorporates terms that penalize anatomically inconsistent predictions (e.g., nodules outside lungs) and reinforces valid spatial relationships. Parameter optimization occurs at multiple levels, balancing task-specific performance with hierarchical consistency.
Data Source
AI summary
A medical image processing apparatus includes processing circuitry configured to apply a first trained model to input image data to obtain a first output based on the input data, where the input data includes clinical data. The processing circuitry is further configured to apply a second trained model to the input data to obtain a second output based on the input data, where the first trained model and the second trained model have been trained in dependence on a hierarchical relationship between the first output and the second output. The hierarchical relationship includes at least one of: a spatial hierarchy, a temporal hierarchy, an anatomical hierarchy, and a hierarchy of clinical conditions.