关于《通过自监督深度学习实现全切片图像的快速可扩展搜索》的评论
Comments on 'Fast and scalable search of whole-slide images via self-supervised deep learning'
AI总结：
本文是针对Chen等人提出的SISH组织学图像搜索方法的评论，指出该方法是Yottixel的增量改进、未引用MinMax二值化原始文献且命名不当，同时提及实验对比方面的其他问题。
摘要：
Chen等人[Chen2022]近期在《自然·生物医学工程》上发表了题为《通过自监督深度学习实现全切片图像的快速可扩展搜索》的文章。作者将其方法命名为“组织学自监督图像搜索”，简称SISH。我们提出担忧：SISH是对Yottixel的增量式改进，使用了MinMax二值化却未引用原始文献，且基于“自监督图像搜索”这一不当命名。此外，我们还指出了Chen等人在实验与对比设置方面的若干其他问题。
英文摘要：
Chen et al. [Chen2022] recently published the article 'Fast and scalable search of whole-slide images via self-supervised deep learning' in Nature Biomedical Engineering. The authors call their method 'self-supervised image search for histology', short SISH. We express our concerns that SISH is an incremental modification of Yottixel, has used MinMax binarization but does not cite the original works, and is based on a misnomer 'self-supervised image search'. As well, we point to several other concerns regarding experiments and comparisons performed by Chen et al.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
