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LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype
2023-03-17
pathology
Title + abstract only
View on medRxiv
Show abstract
Lymphomas vary in terms of clinical behavior, morphology, and response to therapies and thus accurate classification is essential for appropriate management of patients. In this study, using a set of 670 cases of lymphoma obtained from a center in Guatemala City, we propose an interpretable machine learning method, LymphoML, for lymphoma subtyping into eight diagnostic categories. LymphoML sequentially applies steps of (1) object segmentation to extract nuclei, cells, and cytoplasm from hematoxy...
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