论文标题
在图像翻译中处理多一对一映射的不对称周期矛盾损失：大腿MR扫描的研究
An Asymmetric Cycle-Consistency Loss for Dealing with Many-to-One Mappings in Image Translation: A Study on Thigh MR Scans
论文作者
论文摘要
使用周期抗性损失的生成对抗网络有助于对图像翻译模型的未配对培训，从而在歧管医学应用中具有很高的潜力。但是，一个域中的图像可能映射到另一个域中的多个图像（例如，在病理变化的情况下）对训练网络构成了重大挑战。在这项工作中，我们提供了一种解决方案，以通过修改周期矛盾损失来改善训练过程。我们正式和经验地表明，所提出的方法可以显着改善性能，而不会根本改变体系结构，而不会增加整体复杂性。我们评估了大腿MRI扫描的方法，其最终目标是将脂肪浸润患者数据中的肌肉分割。
Generative adversarial networks using a cycle-consistency loss facilitate unpaired training of image-translation models and thereby exhibit a very high potential in manifold medical applications. However, the fact that images in one domain potentially map to more than one image in another domain (e.g. in case of pathological changes) exhibits a major challenge for training the networks. In this work, we offer a solution to improve the training process in case of many-to-one mappings by modifying the cycle-consistency loss. We show formally and empirically that the proposed method improves the performance significantly without radically changing the architecture and without increasing the overall complexity. We evaluate our method on thigh MRI scans with the final goal of segmenting the muscle in fat-infiltrated patients' data.
