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

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 mo

SUBMITTER: Mu Y 

PROVIDER: S-EPMC9053264 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature