Qi, Jing 
ORCID: 0000-0003-2476-7620, Burnside, Girvan ORCID: 0000-0001-7398-1346 and Coenen, Frans ORCID: 0000-0003-1026-6649
(2022)
Data Augmentation for Pathology Prioritisation: An Improved LSTM-Based Approach.
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Abstract
Public hospitals receive large volumes of pathology results everyday. It is therefore challenging for doctors to comprehensively analyse all this data. Pathology data prioritisation would seem to provide at least a partial solution. It has been suggested that deep learning techniques can be used to construct pathology data prioritisation models. However, due to the resource required to obtain sufficient prioritisation training and test data, the usage of deep learning, which requires large labelled training data sets, was found not to be viable. The idea presented in this paper is to use a small seed set of labelled data and then to augment this data. The motivation here was that data augmentation had been previously employed successfully to address data scarcity problems. Four data augmentation methods are considered in this paper and used to train deep learning pathology data prioritisation models. Evaluation was conducted using Urea and Electrolytes pathology data. The results show a best recall and precision of 0.73 and 0.71 respectively.
| Item Type: | Conference or Workshop Item (Unspecified) |
|---|---|
| Uncontrolled Keywords: | Data prioritisation, Data augmentation, LSTM, Pathology data |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 17 Oct 2022 08:41 |
| Last Modified: | 08 Mar 2023 11:47 |
| DOI: | 10.1007/978-3-031-21441-7_4 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3165510 |
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