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

Khanh Nguyen, Hoang Huy Nguyen, Aleksei Tiulpin

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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{}.
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Thursday 7th July
Poster Session 2.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)
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