# AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Margin Learning for Forensic Medical Image Matching

arXiv: https://arxiv.org/abs/2205.02849 · HTML: https://mlsanity.com/paper/2205.02849/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 5 May 2022.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [A Metric Learning Reality Check](https://mlsanity.com/paper/2003.08505/) (2020)
- [SoftTriple Loss: Deep Metric Learning Without Triplet Sampling](https://mlsanity.com/paper/1909.05235/) (2019)
- [Hard negative examples are hard, but useful](https://mlsanity.com/paper/2007.12749/) (2020)
- [PyTorch Metric Learning](https://mlsanity.com/paper/2008.09164/) (2020)
- [In Defense of the Triplet Loss Again: Learning Robust Person Re-Identification with Fast Approximated Triplet Loss and Label Distillation](https://mlsanity.com/paper/1912.07863/) (2019)
- [Deep-Person: Learning Discriminative Deep Features for Person Re-Identification](https://mlsanity.com/paper/1711.10658/) (2017)
- [KNEEL: Knee Anatomical Landmark Localization Using Hourglass Networks](https://mlsanity.com/paper/1907.12237/) (2019)

## Built on by (most cited first, 3 of 3)

- [Deep Learning-based Anonymization of Chest Radiographs: A Utility-preserving Measure for Patient Privacy](https://mlsanity.com/paper/2209.11531/) (2022)
- [Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss](https://mlsanity.com/paper/2410.20401/) (2024)
- [No Pairs Left Behind: Improving Metric Learning with Regularized Triplet Objective](https://mlsanity.com/paper/2210.09506/) (2022)

## Abstract (arXiv)

This paper tackles the challenge of forensic medical image matching (FMIM) using deep neural networks (DNNs). FMIM is a particular case of content-based image retrieval (CBIR). The main challenge in FMIM compared to the general case of CBIR, is that the subject to whom a query image belongs may be affected by aging and progressive degenerative disorders, making it difficult to match data on a subject level. CBIR with DNNs is generally solved by minimizing a ranking loss, such as Triplet loss (TL), computed on image representations extracted by a DNN from the original data. TL, in particular, operates on triplets: anchor, positive (similar to anchor) and negative (dissimilar to anchor). Although TL has been shown to perform well in many CBIR tasks, it still has limitations, which we identify and analyze in this work. In this paper, we introduce (i) the AdaTriplet loss -- an extension of TL whose gradients adapt to different difficulty levels of negative samples, and (ii) the AutoMargin method -- a technique to adjust hyperparameters of margin-based losses such as TL and our proposed loss dynamically. Our results are evaluated on two large-scale benchmarks for FMIM based on the Osteoarthritis Initiative and Chest X-ray-14 datasets. The codes allowing replication of this study have been made publicly available at urlhttps://github.com/Oulu-IMEDS/AdaTriplet.