Deep Learning-based Intelligent Diagnosis of Congenital Uterine Anomalies in 3D Ultrasound
Published 16 Sept 2026arXiv:2609.15225
Updated 7 h ago · first seen 15 Sept 2026
paper_01M2JK1QPY4GV6R64NPM66Z7JP
Abstract
Objective: To develop an intelligent framework, termed CUA-Net, for the automated classification of congenital uterine anomalies (CUA) without requiring coronal plane reconstruction, and to evaluate its clinical applicability. Methods: CUA-Net was built on 3D ResNet-18, equipped with a dynamic data resampling strategy to mitigate the data imbalance issue and a hard sample mining technique to fully learn from the difficult cases by loss adjustment. We further proposed the self-supervised reconstruction to comprehensively explore the volumes and the online data augmentation to refine the wrong predictions and enhance the model's generalization. We compared the CUA-Net with different deep-learning methods and junior/senior sonographers in the testing set. The evaluation metrics included accuracy, precision, recall, F1-score, micro-AUC, and macro-AUC. Results: The proposed CUA-Net exhibited satisfactory performance in both internal and external test sets. In the internal cohort, the model achieved accuracy of 93.88%, precision of 87.01%, recall of 95.92%, F1-score of 88.09%, and micro-AUC of 0.9982 and macro-AUC of 0.9997. In the external set, it maintained good performance with accuracy of 91.52%, precision of 83.27%, recall of 88.63%, F1-score of 81.49%, micro-AUC of 0.9945 and macro-AUC of 0.9990. Our CUA-Net outperformed the junior sonographers across all performance indicators and achieved performance comparable to that of the senior sonographers across most metrics. Conclusion: The CUA-Net demonstrates favorable accuracy and generalizability in classifying common CUA categories, while showing preliminary potential for recognizing less prevalent anomalies. These capabilities may help optimize clinical workflows and support more standardized diagnosis.
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- Property changedPaperDeep Learning-based Intelligent Diagnosis of Congenital Uterine Anomalies in 3D Ultrasound
Deep Learning-based Intelligent Diagnosis of Congenital Uterine Anomalies in 3D Ultrasound: arxiv announce type changed from new to replace
Arxiv announce typenew→replacearxiv - Property changedPaperDeep Learning-based Intelligent Diagnosis of Congenital Uterine Anomalies in 3D Ultrasound
Deep Learning-based Intelligent Diagnosis of Congenital Uterine Anomalies in 3D Ultrasound: published at changed from 2026-09-15T04:00:00+00:00 to 2026-09-16T04:00:00+00:00
Published15 Sept 2026→16 Sept 2026arxiv - Property changedPaperDeep Learning-based Intelligent Diagnosis of Congenital Uterine Anomalies in 3D Ultrasound
Deep Learning-based Intelligent Diagnosis of Congenital Uterine Anomalies in 3D Ultrasound: authors changed from ["Chengcai Chen", "Dong Ni", "Haining Chen", "Haoming Zha… to ["Chengcai Chen", "Dong Ni", "Haining Chen", "Haoming Zha…
AuthorsChengcai Chen, Dong Ni, Haining Chen, Haoming Zhang, Jiajia Qu, Jiaxiao Deng, Shiying Zheng, Xiaomei Tang, Xin Yang, Yiyi Wu, Yuanji Zhang, Yueyue Xu, Yuhao Huang→Chengcai Chen, Dong Ni, Haining Chen, Haoming Zhang, Hongyu Zheng, Jiajia Qu, Jiaxiao Deng, Shiying Zheng, Xiaomei Tang, Xin Yang, Yiyi Wu, Yuanji Zhang, Yueyue Xu, Yuhao Huangarxiv - New paperPaperDeep Learning-based Intelligent Diagnosis of Congenital Uterine Anomalies in 3D Ultrasound
New paper: Deep Learning-based Intelligent Diagnosis of Congenital Uterine Anomalies in 3D Ultrasound
arxiv
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