# Content-Based Medical Image Retrieval with Opponent Class Adaptive Margin Loss

arXiv: https://arxiv.org/abs/2211.15371 · HTML: https://mlsanity.com/paper/2211.15371/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 66 references that are in the map.
- First posted on arXiv 22 Nov 2022.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [Unsupervised Medical Image Translation with Adversarial Diffusion Models](https://mlsanity.com/paper/2207.08208/) (2022)
- [ResViT: Residual vision transformers for multi-modal medical image synthesis](https://mlsanity.com/paper/2106.16031/) (2021)
- [Beyond triplet loss: a deep quadruplet network for person re-identification](https://mlsanity.com/paper/1704.01719/) (2017)
- [Adaptive Diffusion Priors for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2207.05876/) (2022)
- [Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers](https://mlsanity.com/paper/2105.08059/) (2021)
- [A Decade Survey of Content Based Image Retrieval using Deep Learning](https://mlsanity.com/paper/2012.00641/) (2020)
- [A Survey on Deep Hashing Methods](https://mlsanity.com/paper/2003.03369/) (2020)
- [Neural Methods for Point-wise Dependency Estimation](https://mlsanity.com/paper/2006.05553/) (2020)
- [Exploiting Local Features from Deep Networks for Image Retrieval](https://mlsanity.com/paper/1504.05133/) (2015)
- [3D Object Instance Recognition and Pose Estimation Using Triplet Loss with Dynamic Margin](https://mlsanity.com/paper/1904.04854/) (2019)
- [REMAP: Multi-layer entropy-guided pooling of dense CNN features for image retrieval](https://mlsanity.com/paper/1906.06626/) (2019)
- [A weakly supervised adaptive triplet loss for deep metric learning](https://mlsanity.com/paper/1909.12939/) (2019)
- [Deep Ranking with Adaptive Margin Triplet Loss](https://mlsanity.com/paper/2107.06187/) (2021)
- [End-to-End Cross-Modality Retrieval with CCA Projections and Pairwise Ranking Loss](https://mlsanity.com/paper/1705.06979/) (2017)
- [Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets](https://mlsanity.com/paper/2204.02275/) (2022)
- [Combating Ambiguity for Hash-code Learning in Medical Instance Retrieval](https://mlsanity.com/paper/2105.08872/) (2021)
- [Learning to embed semantic similarity for joint image-text retrieval](https://mlsanity.com/paper/2210.03838/) (2022)

## Built on by (most cited first, 7 of 7)

- [ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax](https://mlsanity.com/paper/2303.01615/) (2023)
- [HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Images](https://mlsanity.com/paper/2310.06143/) (2023)
- [Leveraging Foundation Models for Content-Based Image Retrieval in Radiology](https://mlsanity.com/paper/2403.06567/) (2024)
- [USL-Net: Uncertainty Self-Learning Network for Unsupervised Skin Lesion Segmentation](https://mlsanity.com/paper/2309.13289/) (2023)
- [Learning from the few: Fine-grained approach to pediatric wrist pathology recognition on a limited dataset](https://mlsanity.com/paper/2408.13542/) (2024)
- [RadiomicsRetrieval: A Customizable Framework for Medical Image Retrieval Using Radiomics Features](https://mlsanity.com/paper/2507.08546/) (2025)
- [Advanced Multi-Architecture Deep Learning Framework for BIRADS-Based Mammographic Image Retrieval: Comprehensive Performance Analysis with Super-Ensemble Optimization](https://mlsanity.com/paper/2508.04790/) (2025)

## Abstract (arXiv)

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.