# Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval

arXiv: https://arxiv.org/abs/2011.06224 · HTML: https://mlsanity.com/paper/2011.06224/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2020.

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

- [Learning Disentangled Representations in the Imaging Domain](https://mlsanity.com/paper/2108.12043/) (2021)
- [Disentanglement via Latent Quantization](https://mlsanity.com/paper/2305.18378/) (2023)
- [Sketch-based Medical Image Retrieval](https://mlsanity.com/paper/2303.03633/) (2023)
- [Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval](https://mlsanity.com/paper/2103.12328/) (2021)
- [CoBooM: Codebook Guided Bootstrapping for Medical Image Representation Learning](https://mlsanity.com/paper/2408.04262/) (2024)
- [DisMix: Order-Aware Mixup for Medical Imaging via Disentangling Ordinal and Non-Ordinal Features](https://mlsanity.com/paper/2608.04652/) (2026)

## Abstract (arXiv)

Medical images can be decomposed into normal and abnormal features, which is considered as the compositionality. Based on this idea, we propose an encoder-decoder network to decompose a medical image into two discrete latent codes: a normal anatomy code and an abnormal anatomy code. Using these latent codes, we demonstrate a similarity retrieval by focusing on either normal or abnormal features of medical images.