论文标题
通过卷积神经网络对心脏MRI的几何转化预测
Prediction of Geometric Transformation on Cardiac MRI via Convolutional Neural Network
论文作者
论文摘要
在医学图像领域，深层卷积神经网络（Convnets）在分类，细分和注册任务方面取得了巨大成功，这要归功于其学习图像功能的无与伦比的能力。但是，这些任务通常需要大量的手动注释数据，并且是劳动密集型的。因此，研究无监督的语义特征学习任务至关重要。在我们的工作中，我们建议通过训练Convnets来学习医学图像中的特征，以识别应用于图像的几何转换，并提出一个简单的自我监督任务，可以轻松预测几何变换。我们精确地以数学术语定义了一组几何变换，并考虑到空间维度和时间维度之间的区别，并将该模型推广到3D。我们评估了不同模态（BSSFP，T2，LGE）的CMR图像的自我监督方法，并分别达到96.4％，97.5％和96.4％的精确度。我们论文的代码和模型将在：https：//github.com/gaoxin492/geometric_transformation_cmr上发布：
In the field of medical image, deep convolutional neural networks(ConvNets) have achieved great success in the classification, segmentation, and registration tasks thanks to their unparalleled capacity to learn image features. However, these tasks often require large amounts of manually annotated data and are labor-intensive. Therefore, it is of significant importance for us to study unsupervised semantic feature learning tasks. In our work, we propose to learn features in medical images by training ConvNets to recognize the geometric transformation applied to images and present a simple self-supervised task that can easily predict the geometric transformation. We precisely define a set of geometric transformations in mathematical terms and generalize this model to 3D, taking into account the distinction between spatial and time dimensions. We evaluated our self-supervised method on CMR images of different modalities (bSSFP, T2, LGE) and achieved accuracies of 96.4%, 97.5%, and 96.4%, respectively. The code and models of our paper will be published on: https://github.com/gaoxin492/Geometric_Transformation_CMR
