English(EN) Interpretable Deep Learning Models for Breast Cancer Diagnosis Using Medical Imaging

深度学习模型在乳腺癌诊断方面进行基准测试

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员评估了十种深度学习模型，包括DenseNet121等卷积神经网络（CNN）以及ViT和Swin Transformer等Transformer模型，用于诊断医学影像中的乳腺癌的有效性。DenseNet121的准确率最高，达到99.4%，AUC-ROC为0.999，显著优于其他模型，特别是基于Transformer的架构。该研究还采用了Grad-CAM和LIME等可解释人工智能技术，以确保模型的预测具有可解释性并关注诊断相关区域，为开发准确且易于理解的诊断工具指明了方向。 AI

影响通过提高可解释性，增强了人工智能在医学诊断中可信度和潜在的临床应用。

排序理由学术论文，详细介绍了模型在特定任务上的性能和可解释性。[lever_c_demoted from research: ic=1 ai=1.0]

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Signal score: 11 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 学术论文，详细介绍了模型在特定任务上的性能和可解释性。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, model release; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Sami Alnawali, Muhammad Atif Saeed, Abdul Hadi, Azka Atiq, Akhtar Jamil, Ahmad Din · 2026-10-28 04:00

使用医学影像的可解释深度学习模型用于乳腺癌诊断

arXiv:2610.37026v1 Announce Type: cross Abstract: Deep learning shows promise for breast cancer diagnosis, but limited interpretability hinders clinical adoption. We benchmark ten ImageNet-pretrained models (ResNet50, VGG19, InceptionV3, DenseNet121, Xception, MobileNetV2, Effici…

本文如何被排名

报道来源 [1]

使用医学影像的可解释深度学习模型用于乳腺癌诊断

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