FOAL: Fast Online Adaptive Learning for Cardiac Motion Estimation

2020/03/10 by Hanchao Yu, Shanhui Sun, Yu, Hanchao +9 · 2 citations
Computer Science · Medicine · #Advanced MRI Techniques and Applications #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Medical Image Segmentation Techniques #Medical Imaging Techniques and Applications #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2003.04492

openalex publication_date 2020/03/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Motion estimation of cardiac MRI videos is crucial for the evaluation of human heart anatomy and function. Recent researches show promising results with deep learning-based methods. In clinical deployment, however, they suffer dramatic performance drops due to mismatched distributions between training and testing datasets, commonly encountered in the clinical environment. On the other hand, it is arguably impossible to collect all representative datasets and to train a universal tracker before deployment. In this context, we proposed a novel fast online adaptive learning (FOAL) framework: an online gradient descent based optimizer that is optimized by a meta-learner. The meta-learner enables the online optimizer to perform a fast and robust adaptation. We evaluated our method through extensive experiments on two public clinical datasets. The results showed the superior performance of FOAL in accuracy compared to the offline-trained tracking method. On average, the FOAL took only 0.4 second per video for online optimization.

Citations

• Learning without Forgetting
• One-Shot Visual Imitation Learning via Meta-Learning
• Few-Shot Learning with Graph Neural Networks
• Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
• Deep Compressed Sensing

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