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Endoscopy is a widely used imaging modality to diagnose and treat diseases inhollow organs as for example the gastrointestinal tract, the kidney and theliver. However, due to varied modalities and use of different imaging protocolsat various clinical centers impose significant challenges when generalisingdeep learning models. Moreover, the assembly of large datasets from differentclinical centers can introduce a huge label bias that renders any learnt modelunusable. Also, when using new modality or presence of images with rarepatterns, a bulk amount of similar image data and their corresponding labelsare required for training these models. In this work, we propose to use afew-shot learning approach that requires less training data and can be used topredict label classes of test samples from an unseen dataset. We propose anovel additive angular margin metric in the framework of prototypical networkin few-shot learning setting. We compare our approach to the severalestablished methods on a large cohort of multi-center, multi-organ, andmulti-modal endoscopy data. The proposed algorithm outperforms existingstate-of-the-art methods.

Additive Angular Margin for Few Shot Learning to Classify Clinical Endoscopy Images