Self-supervised out-of-distribution detection in wireless capsule endoscopy images.
Artif Intell Med
; 143: 102606, 2023 09.
Article
in En
| MEDLINE
| ID: mdl-37673575
ABSTRACT
While deep learning has displayed excellent performance in a broad spectrum of application areas, neural networks still struggle to recognize what they have not seen, i.e., out-of-distribution (OOD) inputs. In the medical field, building robust models that are able to detect OOD images is highly critical, as these rare images could show diseases or anomalies that should be detected. In this study, we use wireless capsule endoscopy (WCE) images to present a novel patch-based self-supervised approach comprising three stages. First, we train a triplet network to learn vector representations of WCE image patches. Second, we cluster the patch embeddings to group patches in terms of visual similarity. Third, we use the cluster assignments as pseudolabels to train a patch classifier and use the Out-of-Distribution Detector for Neural Networks (ODIN) for OOD detection. The system has been tested on the Kvasir-capsule, a publicly released WCE dataset. Empirical results show an OOD detection improvement compared to baseline methods. Our method can detect unseen pathologies and anomalies such as lymphangiectasia, foreign bodies and blood with AUROC>0.6. This work presents an effective solution for OOD detection models without needing labeled images.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Capsule Endoscopy
Type of study:
Diagnostic_studies
Language:
En
Journal:
Artif Intell Med
Journal subject:
INFORMATICA MEDICA
Year:
2023
Document type:
Article
Affiliation country:
Spain
Publication country:
HOLANDA
/
HOLLAND
/
NETHERLANDS
/
NL
/
PAISES BAJOS
/
THE NETHERLANDS