# Semi-supervised Pathology Segmentation with Disentangled Representations

arXiv: https://arxiv.org/abs/2009.02564 · HTML: https://mlsanity.com/paper/2009.02564/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [Learning Disentangled Representations in the Imaging Domain](https://mlsanity.com/paper/2108.12043/) (2021)
- [Is attention all you need in medical image analysis? A review](https://mlsanity.com/paper/2307.12775/) (2023)
- [GLOWin: A Flow-based Invertible Generative Framework for Learning Disentangled Feature Representations in Medical Images](https://mlsanity.com/paper/2103.10868/) (2021)
- [Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data](https://mlsanity.com/paper/2308.15881/) (2023)
- [Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review](https://mlsanity.com/paper/2408.05249/) (2024)

## Abstract (arXiv)

Automated pathology segmentation remains a valuable diagnostic tool in clinical practice. However, collecting training data is challenging. Semi-supervised approaches by combining labelled and unlabelled data can offer a solution to data scarcity. An approach to semi-supervised learning relies on reconstruction objectives (as self-supervision objectives) that learns in a joint fashion suitable representations for the task. Here, we propose Anatomy-Pathology Disentanglement Network (APD-Net), a pathology segmentation model that attempts to learn jointly for the first time: disentanglement of anatomy, modality, and pathology. The model is trained in a semi-supervised fashion with new reconstruction losses directly aiming to improve pathology segmentation with limited annotations. In addition, a joint optimization strategy is proposed to fully take advantage of the available annotations. We evaluate our methods with two private cardiac infarction segmentation datasets with LGE-MRI scans. APD-Net can perform pathology segmentation with few annotations, maintain performance with different amounts of supervision, and outperform related deep learning methods.