cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule Diagnosis. (arXiv:2210.16097v1 [eess.IV])

Recently, attempts have been made to reduce annotation requirements in
feature-based self-explanatory models for lung nodule diagnosis. As a
representative, cRedAnno achieves competitive performance with considerably
reduced annotation needs by introducing self-supervised contrastive learning to
do unsupervised feature extraction. However, it exhibits unstable performance
under scarce annotation conditions. To improve the accuracy and robustness of
cRedAnno, we propose an annotation exploitation mechanism by conducting
semi-supervised active learning in the learned semantically meaningful space to
jointly utilise the extracted features, annotations, …

annotation arxiv diagnosis exploitation