对比ConvNeXT与EfficientNet在乳腺癌筛查中的表现，发现前者更优。
Deep Learning for Breast Cancer Detection: Comparative Analysis of ConvNeXT and EfficientNet
- 用ConvNeXT和EfficientNet分析乳腺钼靶图像，识别癌症风险。
- ConvNeXT在RSNA数据集上达94.33% AUC、93.36%准确率、95.13%F-score。
- 适合医疗AI研究者或医学影像工程师参考模型性能优化。
乳腺癌是全球最常见的癌症，2022年导致67万例死亡。自1980年代起，高收入国家推行定期乳腺钼靶筛查，使乳腺癌死亡率下降40%。每日诊断人数持续上升，早期检测与治疗对降低死亡率至关重要。本文比较了ConvNeXT与EfficientNet两种卷积神经网络，在RSNA筛查乳腺钼靶数据集上预测乳腺癌风险的表现。研究涵盖图像预处理、分类与性能评估。结果显示，ConvNeXT在该数据集上取得94.33% AUC、93.36%准确率与95.13% F-score，优于EfficientNet的92.34% AUC、91.47%准确率与93.06% F-score。
原文摘要 · Abstract (English)
Breast cancer is the most commonly occurring cancer worldwide. This cancer caused 670,000 deaths globally in 2022, as reported by the WHO. Yet since health officials began routine mammography screening in age groups deemed at risk in the 1980s, breast cancer mortality has decreased by 40% in high-income nations. Every day, a greater and greater number of people are receiving a breast cancer diagnosis. Reducing cancer-related deaths requires early detection and treatment. This paper compares two convolutional neural networks called ConvNeXT and EfficientNet to predict the likelihood of cancer in mammograms from screening exams. Preprocessing of the images, classification, and performance evaluation are main parts of the whole procedure. Several evaluation metrics were used to compare and evaluate the performance of the models. The result shows that ConvNeXT generates better results with a 94.33% AUC score, 93.36% accuracy, and 95.13% F-score compared to EfficientNet with a 92.34% AUC score, 91.47% accuracy, and 93.06% F-score on RSNA screening mammography breast cancer dataset.
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