Deep active learning for classifying cancer pathology reports.

ABSTRACT:

Background

Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques may mitigate this challenge by reducing the amount of labelled data required to effectively train a model. In this study, we analyze the effectiveness of 11 active learning algorithms on classifying subsite and histology from cancer pathology reports using a Convolutional Neural Network as the text classification model.

Results

We compare the performance of each active learning strategy using two differently sized datasets and two different classification tasks. Our results show that on all tasks and dataset sizes, all active learni

SUBMITTER: De Angeli K 

PROVIDER: S-EPMC7941989 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature