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

Youqing Mu; Hamid R Tizhoosh; Rohollah Moosavi Tayebi; Catherine Ross; Monalisa Sur; Brian Leber; Clinton J V Campbell

doi:10.1038/s43856-021-00008-0

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

Commun Med (Lond). 2021 Jul 5:1:11. doi: 10.1038/s43856-021-00008-0. eCollection 2021.

Authors

Youqing Mu¹, Hamid R Tizhoosh², Rohollah Moosavi Tayebi¹, Catherine Ross^{1

3}, Monalisa Sur^{1

3}, Brian Leber^{1

3}, Clinton J V Campbell^{1

3}

Affiliations

¹ McMaster University, Hamilton, ON Canada.
² Kimia Lab, University of Waterloo, Waterloo, ON Canada.
³ Juravinski Hospital and Cancer Centre, Hamilton, ON Canada.

Abstract

Background: Pathology synopses consist of semi-structured or unstructured text summarizing visual information by observing human tissue. Experts write and interpret these synopses with high domain-specific knowledge to extract tissue semantics and formulate a diagnosis in the context of ancillary testing and clinical information. The limited number of specialists available to interpret pathology synopses restricts the utility of the inherent information. Deep learning offers a tool for information extraction and automatic feature generation from complex datasets.

Methods: Using an active learning approach, we developed a set of semantic labels for bone marrow aspirate pathology synopses. We then trained a transformer-based deep-learning model to map these synopses to one or more semantic labels, and extracted learned embeddings (i.e., meaningful attributes) from the model's hidden layer.

Results: Here we demonstrate that with a small amount of training data, a transformer-based natural language model can extract embeddings from pathology synopses that capture diagnostically relevant information. On average, these embeddings can be used to generate semantic labels mapping patients to probable diagnostic groups with a micro-average F1 score of 0.779 Â ± 0.025.

Conclusions: We provide a generalizable deep learning model and approach to unlock the semantic information inherent in pathology synopses toward improved diagnostics, biodiscovery and AI-assisted computational pathology.

Keywords: Haematological cancer; Pathology.