Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning.

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

SUBMITTER: Manescu P 

PROVIDER: S-EPMC9925435 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature