# Comments on 'Fast and scalable search of whole-slide images via self-supervised deep learning'

arXiv: https://arxiv.org/abs/2304.08297 · HTML: https://mlsanity.com/paper/2304.08297/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 7 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [Yottixel -- An Image Search Engine for Large Archives of Histopathology Whole Slide Images](https://mlsanity.com/paper/1911.08748/) (2019)
- [Pan-Cancer Diagnostic Consensus Through Searching Archival Histopathology Images Using Artificial Intelligence](https://mlsanity.com/paper/1911.08736/) (2019)
- [MinMax Radon Barcodes for Medical Image Retrieval](https://mlsanity.com/paper/1610.00318/) (2016)
- [Deep Barcodes for Fast Retrieval of Histopathology Scans](https://mlsanity.com/paper/1805.08833/) (2018)

## Built on by (most cited first, 5 of 5)

- [On Image Search in Histopathology](https://mlsanity.com/paper/2401.08699/) (2024)
- [Analysis and Validation of Image Search Engines in Histopathology](https://mlsanity.com/paper/2401.03271/) (2024)
- [Creating an Atlas of Normal Tissue for Pruning WSI Patching Through Anomaly Detection](https://mlsanity.com/paper/2310.03106/) (2023)
- [Selection of Distinct Morphologies to Divide & Conquer Gigapixel Pathology Images](https://mlsanity.com/paper/2311.09902/) (2023)
- [Histopathology Slide Indexing and Search: Are We There Yet?](https://mlsanity.com/paper/2306.17019/) (2023)

## Abstract (arXiv)

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.