Efficient Extraction of Pathologies from C-Spine Radiology Reports using Multi-Task Learning
Abstract
Pretrained Transformer based models finetuned on domain specific corpora have changed the landscape of NLP. Generally, if one has multiple tasks on a given dataset, one may finetune different models or use task specific adapters. In this work, we show that a multi-task model can beat or achieve the performance of multiple BERT-based models finetuned on various tasks and various task specific adapter augmented BERT-based models. We validate our method on our internal radiologist's report dataset on cervical spine. We hypothesize that the tasks are semantically close and related and thus multitask learners are powerful classifiers. Our work opens the scope of using our method to radiologist's reports on various body parts.
Cite
@article{arxiv.2204.04544,
title = {Efficient Extraction of Pathologies from C-Spine Radiology Reports using Multi-Task Learning},
author = {Arijit Sehanobish and Nathaniel Brown and Ishita Daga and Jayashri Pawar and Danielle Torres and Anasuya Das and Murray Becker and Richard Herzog and Benjamin Odry and Ron Vianu},
journal= {arXiv preprint arXiv:2204.04544},
year = {2022}
}
Comments
Accepted at 6th International Workshop on Health Intelligence, AAAI-2022. To appear in as a book chapter published by Springer in Studies in Computational Intelligence