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English(EN) HFS-TriNet: A Three-Branch Collaborative Feature Learning Network for Prostate Cancer Classification from TRUS Videos

HFS-TriNet网络改进了经直肠超声视频前列腺癌的分类

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了HFS-TriNet，一种旨在改进经直肠超声（TRUS）视频前列腺癌分类的新型网络。该方法通过采用启发式帧选择策略，解决了TRUS视频分析中的冗余和低信噪比等挑战。该网络包含三个协同分支：一个标准的ResNet50，一个利用预训练SAM进行深度特征提取和时间一致性的大模型分支，以及一个用于边缘信息和去噪的小波变换卷积残差分支。 AI

影响 引入了一种新的医学图像分析深度学习架构，有望提高前列腺癌检测的诊断准确性。

排序理由 这是一篇描述特定医学成像任务新网络架构的研究论文。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

HFS-TriNet网络改进了经直肠超声视频前列腺癌的分类

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这是一篇描述特定医学成像任务新网络架构的研究论文。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Xu Lu, Qianhong Peng, Qihao Zhou, Shaopeng Liu, Xiuqin Ye, Chuan Yang, Yuan Yuan ·

    HFS-TriNet：用于前列腺癌TRUS视频分类的三分支协同特征学习网络

    arXiv:2604.22388v1 Announce Type: new Abstract: Transrectal ultrasound (TRUS) imaging is a cost-effective and non-invasive modality widely used in the diagnosis of prostate cancer. The computer-aided diagnosis (CAD) relying on TRUS images has been extensively investigated recentl…

  2. arXiv cs.CV TIER_1 English(EN) · Yuan Yuan ·

    HFS-TriNet：用于前列腺癌TRUS视频分类的三分支协同特征学习网络

    Transrectal ultrasound (TRUS) imaging is a cost-effective and non-invasive modality widely used in the diagnosis of prostate cancer. The computer-aided diagnosis (CAD) relying on TRUS images has been extensively investigated recently. Compared to static images, TRUS video provide…

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