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Comment
. 2022 Jun 21;3(6):100666.
doi: 10.1016/j.xcrm.2022.100666.

When blockchain meets artificial intelligence: An application to cancer histopathology

Runyu Hong  1 , David Fenyö  2
Affiliations
Comment

When blockchain meets artificial intelligence: An application to cancer histopathology

Runyu Hong et al. Cell Rep Med. .

Abstract

A recent study by Saldanha et al. demonstrates that blockchain-based models outcompeted local models and performed similarly with merged models to predict molecular features from cancer histopathology images. The results reveal the capability of decentralized models in molecular diagnosis of cancer.

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Conflict of interest statement

Declaration of interests The authors declare no competing interests.

Figures

Figure 1
Figure 1
Different model training strategies Conventional merged data training involves data sharing and aggregation (left). FL allows only the weights of the locally trained models to be shared to a centralized coordinator (middle). SL adopts decentralized blockchain-based communication during the training process that shares neither the data nor the models (right). Created with biorender.com.
See this image and copyright information in PMC

Comment on

  • Swarm learning for decentralized artificial intelligence in cancer histopathology.
    Saldanha OL, Quirke P, West NP, James JA, Loughrey MB, Grabsch HI, Salto-Tellez M, Alwers E, Cifci D, Ghaffari Laleh N, Seibel T, Gray R, Hutchins GGA, Brenner H, van Treeck M, Yuan T, Brinker TJ, Chang-Claude J, Khader F, Schuppert A, Luedde T, Trautwein C, Muti HS, Foersch S, Hoffmeister M, Truhn D, Kather JN. Saldanha OL, et al. Nat Med. 2022 Jun;28(6):1232-1239. doi: 10.1038/s41591-022-01768-5. Epub 2022 Apr 25. Nat Med. 2022. PMID: 35469069 Free PMC article.

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