# Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval

arXiv: https://arxiv.org/abs/1711.08490 · HTML: https://mlsanity.com/paper/1711.08490/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2017.

## Built on by (most cited first, 6 of 6)

- [Neural ranking models for document retrieval](https://mlsanity.com/paper/2102.11903/) (2021)
- [Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Framework](https://mlsanity.com/paper/2305.04401/) (2023)
- [Budget-aware Few-shot Learning via Graph Convolutional Network](https://mlsanity.com/paper/2201.02304/) (2022)
- [Searching for Pneumothorax in X-Ray Images Using Autoencoded Deep Features](https://mlsanity.com/paper/2102.06096/) (2021)
- [Augmentation Invariant Manifold Learning](https://mlsanity.com/paper/2211.00460/) (2022)
- [Learning Ensembles of Interpretable Simple Structure](https://mlsanity.com/paper/2502.19602/) (2025)

## Abstract (arXiv)

Deep neural networks have been investigated in learning latent representations of medical images, yet most of the studies limit their approach in a single supervised convolutional neural network (CNN), which usually rely heavily on a large scale annotated dataset for training. To learn image representations with less supervision involved, we propose a deep Siamese CNN (SCNN) architecture that can be trained with only binary image pair information. We evaluated the learned image representations on a task of content-based medical image retrieval using a publicly available multiclass diabetic retinopathy fundus image dataset. The experimental results show that our proposed deep SCNN is comparable to the state-of-the-art single supervised CNN, and requires much less supervision for training.