Interpretable multiple instance learning for hematologic diagnosis from peripheral blood smears

Abstract Background Accurate diagnosis of hematologic malignancies from peripheral blood smears (PBSs) requires integrating cellular morphology and composition across numerous white blood cells. Existing computational approaches predominantly automate single-cell classifications and do not provide h...

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Bibliografiske detaljer

Principais autores:	Siddharth Singi, Shenghuan Sun, Zhanghan Yin, Riya Gupta, Dylan C. Webb, Khawaja H. Bilal, Deepika Dilip, Linlin Wang, Neeraj Kumar, Swaraj Nanda, Nicolas Sanchez, Jacob G. Van Cleave, Brenda Fried, Sean Paulsen, Ethan S. Yan, Ali Kamali, Argho Sarkar, Allyne Manzo, Jeeyeon Baik, Irem S. Isgor, Cesar Colorado-Jimenez, Anthony Cardillo, Leonardo Boiocchi, Aijazuddin Syed, David Kim, Brie Kezlarian-Sachs, Maly Fenelus, Alexander Chan, Mariko Yabe, Samuel I. McCash, Menglei Zhu, Simon Mantha, Orly Ardon, Lauren McVoy, Wenbin Xiao, Mikhail Roshal, Oscar Lin, Ahmet Dogan, Iain Carmichael, Chad Vanderbilt, Gregory M. Goldgof
Format:	Artigo
Sprog:	Inglês
Udgivet:	Nature Portfolio 2026-04-01
Serier:	Communications Medicine
Online adgang:	https://doi.org/10.1038/s43856-026-01558-x
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