论文标题
内镜人工伪影分割和检测的多斑合奏
Multi-Plateau Ensemble for Endoscopic Artefact Segmentation and Detection
论文作者
论文摘要
内窥镜伪影检测挑战包括1）伪影检测，2）语义分割和3）样本外的概括。对于语义分割任务，我们提出了一个具有EdgitionNet作为特征提取器/编码器的FPN（特征金字塔网络）的多平纹集合。为了进行对象检测任务，我们使用了带有Resnet50骨干的视网膜的三个模型集合，并使用Resnext101骨干}使用Resnet50骨架（FPN + DC5）。可以在https://github.com/ubamba98/ead2020上获得针对我们解决问题方法的Pytorch实现。
Endoscopic artefact detection challenge consists of 1) Artefact detection, 2) Semantic segmentation, and 3) Out-of-sample generalisation. For Semantic segmentation task, we propose a multi-plateau ensemble of FPN (Feature Pyramid Network) with EfficientNet as feature extractor/encoder. For Object detection task, we used a three model ensemble of RetinaNet with Resnet50 Backbone and FasterRCNN (FPN + DC5) with Resnext101 Backbone}. A PyTorch implementation to our approach to the problem is available at https://github.com/ubamba98/EAD2020.
