# Computer-Aided Diagnosis of Low Grade Endometrial Stromal Sarcoma (LGESS)

arXiv: https://arxiv.org/abs/2107.05426 · HTML: https://mlsanity.com/paper/2107.05426/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 49 references that are in the map.
- First posted on arXiv 9 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [A Tutorial on Principal Component Analysis](https://mlsanity.com/paper/1404.1100/) (2014)
- [Fast GPU-Enabled Color Normalization for Digital Pathology](https://mlsanity.com/paper/1901.03088/) (2019)
- [Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet](https://mlsanity.com/paper/1907.03220/) (2019)

## Built on by (most cited first, 2 of 2)

- [MedViT: A Robust Vision Transformer for Generalized Medical Image Classification](https://mlsanity.com/paper/2302.09462/) (2023)
- [Medi-CAT: Contrastive Adversarial Training for Medical Image Classification](https://mlsanity.com/paper/2311.00154/) (2023)

## Abstract (arXiv)

Low grade endometrial stromal sarcoma (LGESS) is rare form of cancer, accounting for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdiagnosed with leiomyoma, which is a type of benign tumor, also known as fibroids. In this research, uterine tissue biopsy images of potential LGESS patients are preprocessed using segmentation and staining normalization algorithms. A variety of classic machine learning and leading deep learning models are then applied to classify tissue images as either benign or cancerous. For the classic techniques considered, the highest classification accuracy we attain is about 0.85, while our best deep learning model achieves an accuracy of approximately 0.87. These results indicate that properly trained learning algorithms can play a useful role in the diagnosis of LGESS.