Fig. 1: Generation of semantic labels for bone marrow aspirate synopses and modeling process. | Communications Medicine

Fig. 1: Generation of semantic labels for bone marrow aspirate synopses and modeling process.

From: A BERT model generates diagnostically relevant semantic embeddings from pathology synopses with active learning

Fig. 1: Generation of semantic labels for bone marrow aspirate synopses and modeling process.

An expert reader (a clinical hematologist) interprets semi-structured bone marrow aspirate synopses and maps their contents to one or more semantic labels, which impact clinical decision-making. In order to train a model to assign semantic labels to bone marrow aspirate synopses, a synopsis first becomes a single text string and then tokenized as an input vector. The input vector will go through BERT and the classifier. The final output is a vector of size 21 (the number of semantic labels in our study). It is then compared with the ground truth vector to adjust the network weights.

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