Department of Computer Science, San Jose State University, USA.
Department of Computer Science, San Jose State University, USA.
Comput Biol Med. 2021 Nov;138:104874. doi: 10.1016/j.compbiomed.2021.104874. Epub 2021 Sep 22.
Low grade endometrial stromal sarcoma (LGESS) accounts 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 stain normalization algorithms. We then apply a variety of classic machine learning and advanced deep learning models 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 clearly indicate that properly trained learning algorithms can aid in the diagnosis of LGESS.
低度子宫内膜间质肉瘤 (LGESS) 约占所有子宫癌病例的 0.2%。大约 75%的 LGESS 患者最初被误诊为平滑肌瘤，这是一种良性肿瘤，也称为肌瘤。在这项研究中，我们使用分割和染色归一化算法对可能患有 LGESS 的患者的子宫组织活检图像进行预处理。然后，我们应用各种经典机器学习和先进的深度学习模型来对组织图像进行良性或恶性分类。对于所考虑的经典技术，我们获得的最高分类准确性约为 0.85，而我们最好的深度学习模型的准确性约为 0.87。这些结果清楚地表明，经过适当训练的学习算法可以帮助诊断 LGESS。
