English(EN) ProtoCAM: Interpretable Few-Shot Mask-Guided Prototypical Learning for Breast Lesion Classification in Ultrasound Imaging

新的ProtoCAM框架改进了少样本乳腺病变分类

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了ProtoCAM，一个新颖的可解释少样本学习框架，专门用于超声影像中的乳腺病变分类。该方法集成了掩码引导特征编码和原型度量学习，以在有限的训练数据下提高分类准确性。在BUSI数据集上的评估显示，ProtoCAM在3路5样本设置下达到了0.910的宏观F1分数，使用ResNet18作为骨干网络在15样本配置下达到了91.65%的准确率，并提供了可解释的诊断决策见解。 AI

影响通过提高AI模型在低数据场景下的准确性和可解释性，增强了医学影像的诊断能力。

排序理由该项目是一篇研究论文，详细介绍了一种用于医学影像的新机器学习框架。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Ashkan Ebadi · 2026-09-15 04:00

ProtoCAM：用于超声影像乳腺病灶分类的可解释少样本掩码引导原型学习

arXiv:2609.13340v1 Announce Type: cross Abstract: Breast ultrasound imaging plays an important role in the early detection and diagnosis of breast cancer, particularly for patients with dense breast tissue. However, developing reliable deep learning models for ultrasound analysis…

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ProtoCAM：用于超声影像乳腺病灶分类的可解释少样本掩码引导原型学习

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