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As seen in: Nature, Archives of Pathology

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Interpretable multiple instance learning for hematologic diagnosis from peripheral blood smears

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By Siddharth Singi, Shenghuan Sun, RIYA Gupta, Khawaja Bilal, Deepika Dilip, Linlin Wang, Neeraj Kumar, Nicolas Sanchez, Nicolás Galarza Sánchez, Sean Paulsen, Ethan Yan, Ali Reza Kamali, Jeeyeon Baik, Anthony Cardillo, Leonardo Boiocchi, Aijazuddin Syed, Brie E. Kezlarian-Sachs, Maly Fenelus, Alexander Chan, Mariko Yabe, Menglei Zhu, Simon Mantha, Orly Ardon, Wenbin Xiao, Mikhail Roshal, Oscar Lin, Ahmet Dogan, Iain Carmichael, Chad Vanderbilt, Gregory Goldgof, Zhanghan Yin, Swaraj Nanda, Jacob G. Van Cleave, Brenda Fried, Argho Sarkar, Allyne Manzo, Irem S. Isgor, Cesar Colorado-Jimenez, Samuel I. McCash, Lauren McVoy
| Nature Verified
Abstract 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 holistic, slide-level diagnostic predictions.
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