Progressive Multi-Task Network for Medical Scan Patient Modeling
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Solution Overview
Problem
Current medical scanning technologies rely on separate models for each patient modeling task, which are highly correlated, leading to inefficiencies and error propagation when cascaded together, as they do not leverage shared input data effectively.
Innovation Solution
A progressive multi-task model is used where features learned from one task are shared and used as constants in subsequent tasks, allowing for efficient and accurate estimation of multiple patient characteristics such as landmarks, pose, body shape, weight, and internal markers, reducing model complexity and computational power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate models are used for each patient modeling task, then each task can be solved independently, but computational efficiency decreases and errors propagate when tasks are cascaded together
Solution Approach 1:
The patent combines multiple separate patient modeling tasks (landmark detection, pose estimation, body shape estimation, weight/height estimation) into a unified progressive multi-task network. This single integrated model processes surface data once and generates all patient characteristics simultaneously, eliminating redundant computations and error propagation between cascaded separate models while maintaining high accuracy for each individual task.
Solution Approach 2:
The progressive multi-task network is designed as a universal model that performs multiple patient modeling functions through a shared feature extraction backbone. The network uses common learned features for all tasks (landmarks, pose, shape, weight, height) and then applies task-specific heads, allowing one model to replace multiple separate models and significantly improving computational efficiency.
2Adaptability or versatility
If multiple separate models are cascaded together to solve patient modeling tasks, then comprehensive patient characteristics can be obtained, but model complexity and resource consumption increase
Solution Approach 1:
The patent merges five separate patient modeling tasks into a single progressive multi-task network with a shared backbone. This unified architecture reduces model complexity by eliminating redundant feature extraction processes that would exist in cascaded separate models, while still providing comprehensive patient characteristics including landmarks, pose, body shape, weight, and height.
Solution Approach 2:
The unified model is segmented into a shared feature extraction backbone and task-specific output heads. This segmentation allows the complex multi-task model to be managed efficiently, with the shared backbone handling common feature extraction and separate heads handling specific task requirements, making the overall system more tractable than fully separate models.
3Measurement precision
If separate solutions are run in parallel or cascaded for each patient modeling task, then each task receives dedicated processing, but the correlated nature of tasks and shared input data is not leveraged
Solution Approach 1:
The patent merges the processing of correlated patient modeling tasks into a single progressive multi-task network that processes shared input surface data once. The network leverages the correlations between tasks by using shared learned features for all tasks, significantly reducing computational resource consumption compared to running separate models in parallel while maintaining or improving measurement precision through joint optimization.
Data Source
AI summary
For training for and performance of patient modeling from surface data in a medical system, a progressive multi-task model is used. Different tasks for scanning are provided, such as landmark estimation and patient pose estimation. One or more features learned for one task are used as fixed or constant features in the other task. This progressive approach based on shared features increases efficiency while avoiding reductions in accuracy for any given task.