Comments on 'Fast and scalable search of whole-slide images via self-supervised deep learning'
This is an incremental critique addressing concerns about methodological novelty and experimental integrity in a biomedical imaging study.
The authors critique a recent paper on fast and scalable search of whole-slide images via self-supervised deep learning, arguing that the method is an incremental modification of existing work, lacks proper citations, and misuses terminology.
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.