Deep Learning for Diagnosis of Endometrial Cancer and Atypical Endometrial Hyperplasia
2023 IEEE 4th International Conference on Pattern Recognition and Machine Learning (PRML)(2023)
摘要
Endometrial cancer(EC) is the most common and rapidly increasing female cancer globally. Atypical endometrial hyperplasia (AEH) is a precancerous condition of EC. Although hysteroscopy serves as the primary modality for diagnosing lesions, it relies on the subjective judgment of hysteroscopists. Therefore, this study proposed a computer-aided diagnostic system utilizing the EfficientNet network as a baseline, incorporating ParNet attention mechanism and class weighting to accurately classify EC/AEH from benign lesions. This study included 49,556 hysteroscopy images from 1,237 cases as a training set and 3,412 hysteroscopy images from 85 cases as a testing set. AUC, accuracy, sensitivity, specificity, PPV, Kappa, and F
1
-Score of the proposed method are 0.941, 89.4%, 93.7%, 87.1%, 73.3%, 0.755, and 0.8225, respectively. The proposed model may be used as a computer-aided tool for the diagnosis of EC/AEH.
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关键词
Hysteroscopy,deep learning,endometrial cancer,atypical endometrial hyperplasia,EfficientNet
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