Padi, S;
Manescu, P;
Schaub, N;
Hotaling, N;
Simon, C;
Bharti, K;
Bajcsy, P;
(2020)
Comparison of Artificial Intelligence based approaches to cell function prediction.
Informatics in Medicine Unlocked
, 18
, Article 100270. 10.1016/j.imu.2019.100270.
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Abstract
Predicting Retinal Pigment Epithelium (RPE) cell functions in stem cell implants using non-invasive bright field microscopy imaging is a critical task for clinical deployment of stem cell therapies. Such cell function predictions can be carried out using Artificial Intelligence (AI) based models. In this paper we used Traditional Machine Learning (TML) and Deep Learning (DL) based AI models for cell function prediction tasks. TML models depend on feature engineering and DL models perform feature engineering automatically but have higher modeling complexity. This work aims at exploring the tradeoffs between three approaches using TML and DL based models for RPE cell function prediction from microscopy images and at understanding the accuracy relationship between pixel-, cell feature-, and implant label-level accuracies of models. Among the three compared approaches to cell function prediction, the direct approach to cell function prediction from images is slightly more accurate in comparison to indirect approaches using intermediate segmentation and/or feature engineering steps. We also evaluated accuracy variations with respect to model selections (five TML models and two DL models) and model configurations (with and without transfer learning). Finally, we quantified the relationships between segmentation accuracy and the number of samples used for training a model, segmentation accuracy and cell feature error, and cell feature error and accuracy of implant labels. We concluded that for the RPE cell data set, there is a monotonic relationship between the number of training samples and image segmentation accuracy, and between segmentation accuracy and cell feature error, but there is no such a relationship between segmentation accuracy and accuracy of RPE implant labels.
| Type: | Article |
|---|---|
| Title: | Comparison of Artificial Intelligence based approaches to cell function prediction |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.imu.2019.100270 |
| Publisher version: | http://doi.org/10.1016/j.imu.2019.100270 |
| Language: | English |
| Additional information: | Copyright © 2019 Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Comparison of machine learning and deep learning models, Cell segmentation, Cell function prediction, Retinal, Pigment, Epithelium Cell, Age-related macular degeneration, Trans-Epithelial Resistance, Vascular Endothelial Growth Factor |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10088584 |
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