论文标题
通过自学学习将任务知识嵌入到3D神经网络中
Embedding Task Knowledge into 3D Neural Networks via Self-supervised Learning
论文作者
论文摘要
深度学习高度依赖于注释数据的数量。但是，注释医学图像非常费力且昂贵。为此，自我监管的学习（SSL）是用于缺乏注释数据的潜在解决方案，吸引了社区的关注。但是，SSL方法通常设计一个不一定与目标任务相关的代理任务。在本文中，我们提出了一种用于3D医学图像分类的新型SSL方法，即与任务相关的对比预测编码（TCPC），将任务知识嵌入到培训3D神经网络中。提出的TCPC首先使用简单线性迭代聚类通过Supervoxel估计来定位初始候选病变。然后，我们从潜在病变区域中裁剪的子体积中提取特征，并为自我监督学习构建校准的对比预测编码方案。广泛的实验是在公共和私人数据集上进行的。实验结果证明了将病变相关的先验知识嵌入到神经网络中的有效性，以进行3D医学图像分类。
Deep learning highly relies on the amount of annotated data. However, annotating medical images is extremely laborious and expensive. To this end, self-supervised learning (SSL), as a potential solution for deficient annotated data, attracts increasing attentions from the community. However, SSL approaches often design a proxy task that is not necessarily related to target task. In this paper, we propose a novel SSL approach for 3D medical image classification, namely Task-related Contrastive Prediction Coding (TCPC), which embeds task knowledge into training 3D neural networks. The proposed TCPC first locates the initial candidate lesions via supervoxel estimation using simple linear iterative clustering. Then, we extract features from the sub-volume cropped around potential lesion areas, and construct a calibrated contrastive predictive coding scheme for self-supervised learning. Extensive experiments are conducted on public and private datasets. The experimental results demonstrate the effectiveness of embedding lesion-related prior-knowledge into neural networks for 3D medical image classification.
