全自动机器学习管道超声心动图分割

Hang Duong Thi Thuy, T. Minh, Phi Nguyen Van, Long Tran Quoc
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摘要

目前,心脏诊断在很大程度上依赖于左心室功能的评估。在分割深度学习模型的帮助下,左心室的评估变得更加容易和准确。然而,深度学习技术仍然面临两个主要障碍:难以获得足够的训练数据和开发高质量模型耗时。在普通的数据采集过程中,数据集是从大量未标记的图像中随机抽取的,这导致对这些图像进行标注需要大量的劳动时间。此外,手工设计的模型开发是费力的,也是昂贵的。本文介绍了一种基于主动学习的管道来简化标注工作,并利用神经结构搜索的思想来自动设计适当的深度学习模型。我们称之为超声心动图分割的全自动机器学习管道。实验结果表明,我们的方法仅在原始训练数据集的2 / 5的情况下获得了与原始训练数据集相同的IOU精度,并且在相同的训练数据集下,搜索到的模型与手工设计的模型具有相同的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fully Automated Machine Learning Pipeline for Echocardiogram Segmentation
Nowadays, cardiac diagnosis largely depends on left ventricular function assessment. With the help of the segmentation deep learning model, the assessment of the left ventricle becomes more accessible and accurate. However, deep learning technique still faces two main obstacles: the difficulty in acquiring sufficient training data and time consuming in developing quality models. In the ordinary data acquisition process, the dataset was selected randomly from a large pool of unlabeled images for labeling, leading to massive labor time to annotate those images. Besides that, hand-designed model development is strenuous and also costly. This paper introduces a pipeline that relies on Active Learning to ease the labeling work and utilizes Neural Architecture Search's idea to design the adequate deep learning model automatically. We called this Fully automated machine learning pipeline for echocardiogram segmentation. The experiment results show that our method obtained the same IOU accuracy with only two-fifths of the original training dataset, and the searched model got the same accuracy as the hand-designed model given the same training dataset.
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