RT Journal Article
T1 Performance of convolutional neural networks for polyp localization on public colonoscopy image datasets
A1 Nogueira Rodríguez, Alba
A1 Reboiro Jato, Miguel
A1 González Peña, Daniel
A1 López Fernández, Hugo
K1 1203.04 Inteligencia Artificial
K1 1203.20 Sistemas de Control Medico
K1 3207.03 Carcinogénesis
AB Colorectal cancer is one of the most frequent malignancies. Colonoscopy is the de facto standard for precancerous lesion detection in the colon, i.e., polyps, during screening studies or after facultative recommendation. In recent years, artificial intelligence, and especially deep learning techniques such as convolutional neural networks, have been applied to polyp detection and localization in order to develop real-time CADe systems. However, the performance of machine learning models is very sensitive to changes in the nature of the testing instances, especially when trying to reproduce results for totally different datasets to those used for model development, i.e., inter-dataset testing. Here, we report the results of testing of our previously published polyp detection model using ten public colonoscopy image datasets and analyze them in the context of the results of other 20 state-of-the-art publications using the same datasets. The F1-score of our recently published model was 0.88 when evaluated on a private test partition, i.e., intra-dataset testing, but it decayed, on average, by 13.65% when tested on ten public datasets. In the published research, the average intra-dataset F1-score is 0.91, and we observed that it also decays in the inter-dataset setting to an average F1-score of 0.83.
PB Diagnostics
SN 20754418
YR 2022
FD 2022-04-04
LK http://hdl.handle.net/11093/3541
UL http://hdl.handle.net/11093/3541
LA eng
NO Diagnostics, 12(4): 898 (2022)
NO Agencia Estatal de Investigación | Ref. DPI2017-87494-R
DS Investigo
RD 24-ene-2025