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

Khanh Nguyen, Hoang Huy Nguyen, Aleksei Tiulpin

Keywords: Deep Learning, Content-based Image Retrieval, Forensic Matching

TL;DR: We present an extended version of triplet loss that is robust to visual changes due to aging and disease progression for forensic medical image matching.

Abstract:

This paper tackles the challenge of forensic medical image matching (FMIM) using deep neural networks (DNNs). We investigate Triplet loss (TL), which is probably the most well-known loss for this problem. TL aims to enforce closeness between similar and enlarge the distance between dissimilar data points in the image representation space extracted by a DNN. Although TL has been shown to perform well, it still has limitations, which we identify and analyze in this work.
Specifically, we first introduce AdaTriplet -- an extension of TL that aims to adapt loss gradients according to the levels of difficulty of negative samples. Second, we also introduce AutoMargin -- a technique to adjust hyperparameters of margin-based losses such as TL and AdaTriplet dynamically during training. The performance of our loss is evaluated on a new large-scale benchmark for FMIM, which we have constructed from the Osteoarthritis Initiative cohort. The codes allowing replication of our results have been made publicly available at \url{https://github.com/Oulu-IMEDS/AdaTriplet}.

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Paper Type: recently published or submitted journal contributions

Primary Subject Area: Unsupervised Learning and Representation Learning

Secondary Subject Area: Application: Radiology

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