@article{discovery10165097,
         journal = {Scientific Reports},
          volume = {13},
          number = {1},
            note = {Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.},
            year = {2023},
           title = {Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning},
       publisher = {Springer Science and Business Media LLC},
           month = {February},
             url = {https://doi.org/10.1038/s41598-023-29160-4},
        abstract = {While optical microscopy inspection of blood films and bone marrow aspirates by a hematologist is a crucial step in establishing diagnosis of acute leukemia, especially in low-resource settings where other diagnostic modalities are not available, the task remains time-consuming and prone to human inconsistencies. This has an impact especially in cases of Acute Promyelocytic Leukemia (APL) that require urgent treatment. Integration of automated computational hematopathology into clinical workflows can improve the throughput of these services and reduce cognitive human error. However, a major bottleneck in deploying such systems is a lack of sufficient cell morphological object-labels annotations to train deep learning models. We overcome this by leveraging patient diagnostic labels to train weakly-supervised models that detect different types of acute leukemia. We introduce a deep learning approach, Multiple Instance Learning for Leukocyte Identification (MILLIE), able to perform automated reliable analysis of blood films with minimal supervision. Without being trained to classify individual cells, MILLIE differentiates between acute lymphoblastic and myeloblastic leukemia in blood films. More importantly, MILLIE detects APL in blood films (AUC 0.94 {$\pm$} 0.04) and in bone marrow aspirates (AUC 0.99 {$\pm$} 0.01). MILLIE is a viable solution to augment the throughput of clinical pathways that require assessment of blood film microscopy.},
          author = {Manescu, Petru and Narayanan, Priya and Bendkowski, Christopher and Elmi, Muna and Claveau, Remy and Pawar, Vijay and Brown, Biobele J and Shaw, Mike and Rao, Anupama and Fernandez-Reyes, Delmiro},
        keywords = {Acute lymphocytic leukaemia,
Acute myeloid leukaemia,
Computational science,
Leukaemia,
Pathology}
}