论文标题
用于医学成像应用的判别跨模式数据增强
Discriminative Cross-Modal Data Augmentation for Medical Imaging Applications
论文作者
论文摘要
尽管深度学习方法在医学图像分析中取得了巨大的成功，但它们需要许多医学图像才能训练。由于数据隐私问题和医疗注释不可用，通常很难获得大量标记的医学图像进行模型培训。在本文中，我们研究了跨模式数据增强，以减轻医学成像域中的数据缺陷问题。我们提出了一个歧视性的未配对的图像到图像翻译模型，该模型将源模态中的图像转换为目标模式中的图像，其中翻译任务是通过下游预测任务共同进行的，并且转换由预测指导。对两种应用的实验证明了我们方法的有效性。
While deep learning methods have shown great success in medical image analysis, they require a number of medical images to train. Due to data privacy concerns and unavailability of medical annotators, it is oftentimes very difficult to obtain a lot of labeled medical images for model training. In this paper, we study cross-modality data augmentation to mitigate the data deficiency issue in the medical imaging domain. We propose a discriminative unpaired image-to-image translation model which translates images in source modality into images in target modality where the translation task is conducted jointly with the downstream prediction task and the translation is guided by the prediction. Experiments on two applications demonstrate the effectiveness of our method.
