计算机科学
强化学习
人工智能
任务(项目管理)
机器学习
学习迁移
适应(眼睛)
多任务学习
图像质量
质量(理念)
元学习(计算机科学)
马尔可夫决策过程
模式识别(心理学)
图像(数学)
马尔可夫过程
管理
经济
物理
哲学
光学
认识论
统计
数学
作者
Shaheer U. Saeed,Yunguan Fu,Vasilis Stavrinides,Zachary M. C. Baum,Qianye Yang,Mirabela Rusu,Richard E. Fan,Geoffrey A. Sonn,J. Alison Noble,Dean C. Barratt,Yipeng Hu
标识
DOI:10.1007/978-3-030-87583-1_19
摘要
The performance of many medical image analysis tasks are strongly associated with image data quality. When developing modern deep learning algorithms, rather than relying on subjective (human-based) image quality assessment (IQA), task amenability potentially provides an objective measure of task-specific image quality. To predict task amenability, an IQA agent is trained using reinforcement learning (RL) with a simultaneously optimised task predictor, such as a classification or segmentation neural network. In this work, we develop transfer learning or adaptation strategies to increase the adaptability of both the IQA agent and the task predictor so that they are less dependent on high-quality, expert-labelled training data. The proposed transfer learning strategy re-formulates the original RL problem for task amenability in a meta-reinforcement learning (meta-RL) framework. The resulting algorithm facilitates efficient adaptation of the agent to different definitions of image quality, each with its own Markov decision process environment including different images, labels and an adaptable task predictor. Our work demonstrates that the IQA agents pre-trained on non-expert task labels can be adapted to predict task amenability as defined by expert task labels, using only a small set of expert labels. Using 6644 clinical ultrasound images from 249 prostate cancer patients, our results for image classification and segmentation tasks show that the proposed IQA method can be adapted using data with as few as respective 19.7\(\%\) and 29.6\(\%\) expert-reviewed consensus labels and still achieve comparable IQA and task performance, which would otherwise require a training dataset with 100\(\%\) expert labels.
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