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

• Published in: Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 Vol. 13438; pp. 725 - 735
• Main Authors: Nguyen, Khanh, Nguyen, Huy Hoang, Tiulpin, Aleksei
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer 2022; Springer Nature Switzerland
• Series: Lecture Notes in Computer Science
• Subjects: Content-based image retrieval; Deep learning; Forensic matching
• ISBN: 3031164512; 9783031164514
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-031-16452-1_69

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 https://github.com/Oulu-IMEDS/AdaTriplet.