We introduce LYSTO, the Lymphocyte Assessment Hackathon, which was held in conjunction with the MICCAI 2019 Conference in Shenzen (China). The competition required participants to automatically assess the number of lymphocytes, in particular T-cells, in histopathological images of colon, breast, and prostate cancer stained with CD3 and CD8 immunohistochemistry. Differently from other challenges setup in medical image analysis, LYSTO participants were solely given a few hours to address this problem. In this paper, we describe the goal and the multi-phase organization of the hackathon; we describe the proposed methods and the on-site results. Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists. We show that some of the participants were capable to achieve pathologist-level performance at lymphocyte assessment. After the hackathon, LYSTO was left as a lightweight plug-and-play benchmark dataset on grand-challenge website, together with an automatic evaluation platform. LYSTO has supported a number of research in lymphocyte assessment in oncology. LYSTO will be a long-lasting educational challenge for deep learning and digital pathology, it is available at https://lysto.grand-challenge.org/.
翻译:我们引入了LYSTO,即与MICCAI 2019会议(中国)一起在沈善(中国)举行的Lymphocyte Aview Hackathon。竞争要求参与者在结肠、乳和前列腺癌的生理病理学图象中自动评估淋巴细胞的数量,特别是T细胞的数量,这些病理学图象中含有CD3和CD8和CD8免疫史化学。不同于医学图像分析中的其他挑战设置,LySTO参与者只得到几个小时来解决这个问题。我们在本文件中描述黑手图的目标和多阶段组织;我们描述拟议的方法和现场结果。此外,我们展示了事后结果,我们展示了在一组独立的肺癌幻灯片上展示的方法是如何表现的,以及将所提出的方法与一个淋巴细胞评估方法与一个病理学家小组相比较。我们显示,一些参与者在深层次的测谎中能够达到病理学家水平的成绩;我们描述了拟议的方法和多阶段组织组织;我们描述了拟议的方法和现场结果;我们展示了后,在黑地研究中,S-LETO一个数据库中,一个数据库数据库中,一个数据库里的一个数据库中,一个数据库里的一个数据库里的数据评估是支持一个数字。