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Image quality assessment for machine learning tasks using meta-reinforcement learning.

Shaheer U Saeed¹, Yunguan Fu², Vasilis Stavrinides³

¹Centre for Medical Image Computing, Wellcome/EPSRC Centre for Interventional & Surgical Sciences, and Department of Medical Physics & Biomedical Engineering, University College London, London, UK.

Medical Image Analysis

|March 28, 2022

Summary

This summary is machine-generated.

This study introduces a new task-specific image quality assessment (IQA) method that predicts image suitability for machine learning tasks. This adaptable approach enhances performance in clinical applications like prostate intervention and pneumonia detection.

Keywords:

Image quality assessment Meta-learning Meta-reinforcement learning Task amenability

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Area of Science:

Computer Vision
Machine Learning
Medical Imaging

Background:

Traditional image quality assessment (IQA) often lacks task-specific relevance.
Evaluating image suitability for machine learning tasks is crucial for reliable predictions.

Purpose of the Study:

To develop a novel, task-specific IQA approach that measures image amenability for downstream machine learning tasks.
To enhance the adaptability of IQA controllers and task predictors for efficient fine-tuning on new datasets.

Main Methods:

Utilized an IQA controller, parameterized by neural networks, trained simultaneously with a task predictor.
Developed a meta-reinforcement learning framework to improve adaptability of both IQA controllers and task predictors.
Applied the approach to clinical applications: ultrasound-guided prostate intervention and X-ray pneumonia detection.

Main Results:

Demonstrated the efficacy of the task-specific, adaptable IQA approach in real-world clinical scenarios.
Showcased improved performance in image classification and segmentation tasks through tailored IQA.

Conclusions:

Task-specific IQA is a more effective measure of image utility for machine learning than generic IQA.
The proposed meta-reinforcement learning framework enables efficient adaptation of IQA and task predictors for diverse medical imaging applications.