Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval
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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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Nov 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 2; 2024: 1; 2025: 0; 2026: 1.
6 papers.
- 2021Learning Disentangled Representations in the Imaging Domaincited by 46
- 2023Disentanglement via Latent Quantizationcited by 40
- 2023Sketch-based Medical Image Retrievalcited by 6
- 2021Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrievalcited by 4
- 2024CoBooM: Codebook Guided Bootstrapping for Medical Image Representation Learningcited by 2
- 2026DisMix: Order-Aware Mixup for Medical Imaging via Disentangling Ordinal and Non-Ordinal Featurescited by 0
- 2026DisMix: Order-Aware Mixup for Medical Imaging via Disentangling Ordinal and Non-Ordinal Features5 Aug 2026
- 2024CoBooM: Codebook Guided Bootstrapping for Medical Image Representation Learning8 Aug 2024
- 2023Disentanglement via Latent Quantization28 May 2023
- 2023Sketch-based Medical Image Retrieval7 Mar 2023
- 2021Learning Disentangled Representations in the Imaging Domain26 Aug 2021
- 2021Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval23 Mar 2021