论文标题
多级结肠镜检查恶性组织检测与对抗CAC-UNET
Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet
论文作者
论文摘要
自动和客观的医学诊断模型对于实现早期癌症检测可能是有价值的，从而降低了死亡率。在本文中，我们提出了通过设计的对抗CAC-UNET进行高效的多级恶性组织检测。采用具有预测前策略和恶性区域标签平滑的贴片级模型来消除负WSI，以降低假阳性检测的风险。对于通过多模型集合所选的关键贴片，对抗性上下文感知和外观一致性UNET（CAC-UNET）旨在实现强大的分割。在CAC-UNET中，镜像设计的歧视器能够无缝融合精巧设计强大的骨干网络的整个特征图，而不会丢失任何信息。此外，还要进一步添加掩码，以通过额外的掩膜域歧视器指导准确的分割掩码预测。提出的方案在MICCAI DigestPath2019挑战结肠镜组织分割和分类任务中取得了最佳结果。完整的实施详细信息和训练有素的模型可在https://github.com/raykoooo/cac-unet上找到。
The automatic and objective medical diagnostic model can be valuable to achieve early cancer detection, and thus reducing the mortality rate. In this paper, we propose a highly efficient multi-level malignant tissue detection through the designed adversarial CAC-UNet. A patch-level model with a pre-prediction strategy and a malignancy area guided label smoothing is adopted to remove the negative WSIs, with which to lower the risk of false positive detection. For the selected key patches by multi-model ensemble, an adversarial context-aware and appearance consistency UNet (CAC-UNet) is designed to achieve robust segmentation. In CAC-UNet, mirror designed discriminators are able to seamlessly fuse the whole feature maps of the skillfully designed powerful backbone network without any information loss. Besides, a mask prior is further added to guide the accurate segmentation mask prediction through an extra mask-domain discriminator. The proposed scheme achieves the best results in MICCAI DigestPath2019 challenge on colonoscopy tissue segmentation and classification task. The full implementation details and the trained models are available at https://github.com/Raykoooo/CAC-UNet.
