The automated segmentation of cancer tissue in histopathology images can help clinicians to detect, diagnose, and analyze such disease. Different from other natural images used in many convolutional networks for benchmark, histopathology images can be extremely large, and the cancerous patterns can reach beyond 1000 pixels. Therefore, the well-known networks in the literature were never conceived to handle these peculiarities. In this work, we propose a Fully Convolutional DenseUNet that is particularly designed to solve histopathology problems. We evaluated our network in two public pathology datasets published as challenges in the recent MICCAI 2019: binary segmentation in colon cancer images (DigestPath2019), and multi-class segmentation in prostate cancer images (Gleason2019), achieving similar and better results than the winners of the challenges, respectively. Furthermore, we discussed some good practices in the training setup to yield the best performance and the main challenges in these histopathology datasets.
翻译:在组织病理学图象中癌症组织的自动分解有助于临床医生检测、诊断和分析这种疾病。不同于许多革命性基准网络使用的其他自然图象,组织病理学图象可能非常巨大,癌症模式可能超过1000像素。因此,文献中众所周知的网络从未设想过要处理这些特殊性。在这项工作中,我们建议建立一个完全的进化共振元件,它特别旨在解决组织病理学问题。我们用两个公共病理数据集评估了我们的网络,这两个公共病理数据集最近发表于MICCAI 2019:结肠癌图象的二元分解(DigestPath192019),以及前列癌症图象的多级分解(Gleason2019),分别取得了与挑战胜出者相似和更好的结果。此外,我们讨论了培训设置中的一些良好做法,以产生最佳表现和这些病理学数据集的主要挑战。