Keywords: AI/ML Software, Machine Learning/Artificial Intelligence, Auto prescription, Scan planning, On-site learning
Motivation: Hospitals have various scan prescription preferences for the same scan target. Data collection and annotation are difficult for in-house development of auto prescription system, especially for breast MRI scan.
Goal(s): Present an on-site learning system that supports auto prescription model learning for MR breast scan preferences.
Approach: Simulate on-site learning system in-house. Automatically generate training data by an anatomy detection model to train auto prescription models learning two orientation preferences.
Results: The average success rate achieved 95% and maximal angle error in 75% cases was less than intra-operator error when training data reached 60 patients.
Impact: The proof-of-concept study verifies the proposed on-site learning system's feasibility by in-house simulation. The system could automatically train auto prescription models adapting to different scan orientation preferences, and it is expected to be extended to other scan ROI parameters.
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