# Efficient feature embedding of 3D brain MRI images for content-based image retrieval with deep metric learning

arXiv: https://arxiv.org/abs/1912.01824 · HTML: https://mlsanity.com/paper/1912.01824/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2019.

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

- [Domain-invariant feature learning in brain MR imaging for content-based image retrieval](https://mlsanity.com/paper/2501.01326/) (2025)
- [Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI](https://mlsanity.com/paper/2108.06518/) (2021)
- [Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval](https://mlsanity.com/paper/2210.00506/) (2022)
- [Enhancing mTBI Diagnosis with Residual Triplet Convolutional Neural Network Using 3D CT](https://mlsanity.com/paper/2311.14197/) (2023)
- [iCBIR-Sli: Interpretable Content-Based Image Retrieval with 2D Slice Embeddings](https://mlsanity.com/paper/2501.01642/) (2025)
- [Acquisition of interpretable domain information during brain MR image harmonization for content-based image retrieval](https://mlsanity.com/paper/2510.14535/) (2025)
- [NeuroCBIR: A Fast and Accurate Image Retrieval System for Whole-Brain and Region-Specific MRI](https://mlsanity.com/paper/2610.06502/) (2026)

## Abstract (arXiv)

Increasing numbers of MRI brain scans, improvements in image resolution, and advancements in MRI acquisition technology are causing significant increases in the demand for and burden on radiologists' efforts in terms of reading and interpreting brain MRIs. Content-based image retrieval (CBIR) is an emerging technology for reducing this burden by supporting the reading of medical images. High dimensionality is a major challenge in developing a CBIR system that is applicable for 3D brain MRIs. In this study, we propose a system called disease-oriented data concentration with metric learning (DDCML). In DDCML, we introduce deep metric learning to a 3D convolutional autoencoder (CAE). Our proposed DDCML scheme achieves a high dimensional compression rate (4096:1) while preserving the disease-related anatomical features that are important for medical image classification. The low-dimensional representation obtained by DDCML improved the clustering performance by 29.1% compared to plain 3D-CAE in terms of discriminating Alzheimer's disease patients from healthy subjects, and successfully reproduced the relationships of the severity of disease categories that were not included in the training.