  Broadspread use of medical imaging devices with digital storage has paved the
way for curation of substantial data repositories. Fast access to image samples
with similar appearance to suspected cases can help establish a consulting
system for healthcare professionals, and improve diagnostic procedures while
minimizing processing delays. However, manual querying of large data
repositories is labor intensive. Content-based image retrieval (CBIR) offers an
automated solution based on dense embedding vectors that represent image
features to allow quantitative similarity assessments. Triplet learning has
emerged as a powerful approach to recover embeddings in CBIR, albeit
traditional loss functions ignore the dynamic relationship between opponent
image classes. Here, we introduce a triplet-learning method for automated
querying of medical image repositories based on a novel Opponent Class Adaptive
Margin (OCAM) loss. OCAM uses a variable margin value that is updated
continually during the course of training to maintain optimally discriminative
representations. CBIR performance of OCAM is compared against state-of-the-art
loss functions for representational learning on three public databases
(gastrointestinal disease, skin lesion, lung disease). Comprehensive
experiments in each application domain demonstrate the superior performance of
OCAM against baselines.
