论文标题
多模式脑肿瘤分类
Multimodal brain tumor classification
论文作者
论文摘要
癌症是一种复杂的疾病，可根据观察的规模提供各种类型的信息。虽然大多数肿瘤诊断是通过观察组织病理学幻灯片来进行的，但放射学图像应为癌症诊断的功效提供更多知识。这项工作研究了一种深度学习方法，结合了整个幻灯片图像和磁共振图像以对肿瘤进行分类。特别是，我们的解决方案包括用于整个幻灯片图像分类的功能强大，通用和模块化的体系结构。实验是针对2020年计算精度医学挑战的前瞻性进行的，该挑战是在3类不平衡的分类任务中进行的。我们报告了跨验验（分别验证）平衡准确性，kappa和f1为0.913、0.897和0.951（分别为0.91、0.90和0.94）。出于研究目的，包括可重复性和直接性能比较，我们的结局提交的模型可在docker图像中可用，请访问https://hub.docker.com/repostority/docker/docker/marvinler/marvinler/cpm_2020_2020_marvinler。
Cancer is a complex disease that provides various types of information depending on the scale of observation. While most tumor diagnostics are performed by observing histopathological slides, radiology images should yield additional knowledge towards the efficacy of cancer diagnostics. This work investigates a deep learning method combining whole slide images and magnetic resonance images to classify tumors. In particular, our solution comprises a powerful, generic and modular architecture for whole slide image classification. Experiments are prospectively conducted on the 2020 Computational Precision Medicine challenge, in a 3-classes unbalanced classification task. We report cross-validation (resp. validation) balanced-accuracy, kappa and f1 of 0.913, 0.897 and 0.951 (resp. 0.91, 0.90 and 0.94). For research purposes, including reproducibility and direct performance comparisons, our finale submitted models are usable off-the-shelf in a Docker image available at https://hub.docker.com/repository/docker/marvinler/cpm_2020_marvinler.
