SSL-CPCD: Self-supervised learning with composite pretext-class
  discrimination for improved generalisability in endoscopic image analysis
Ziang Xu Jens Rittscher Sharib Ali
Data-driven methods have shown tremendous progress in medical image analysis.
In this context, deep learning-based supervised methods are widely popular.
However, they require a large amount of training data and face issues in
generalisability to unseen datasets that hinder clinical translation.
Endoscopic imaging data incorporates large inter- and intra-patient variability
that makes these models more challenging to learn representative features for
downstream tasks. Thus, despite the publicly available datasets and datasets
that can be generated within hospitals, most supervised models still
underperform. While self-supervised learning has addressed this problem to some
extent in natural scene data, there is a considerable performance gap in the
medical image domain. In this paper, we propose to explore patch-level
instance-group discrimination and penalisation of inter-class variation using
additive angular margin within the cosine similarity metrics. Our novel
approach enables models to learn to cluster similar representative patches,
thereby improving their ability to provide better separation between different
classes. Our results demonstrate significant improvement on all metrics over
the state-of-the-art (SOTA) methods on the test set from the same and diverse
datasets. We evaluated our approach for classification, detection, and
segmentation. SSL-CPCD achieves 79.77% on Top 1 accuracy for ulcerative colitis
classification, 88.62% on mAP for polyp detection, and 82.32% on dice
similarity coefficient for segmentation tasks are nearly over 4%, 2%, and 3%,
respectively, compared to the baseline architectures. We also demonstrate that
our method generalises better than all SOTA methods to unseen datasets,
reporting nearly 7% improvement in our generalisability assessment.