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English(EN) Robustness of breast lesion segmentation under MRI undersampling improves with k-space-aware deep learning

深度学习提高了MRI乳腺病变分割的准确性

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种k空间感知深度学习方法，该方法提高了MRI扫描中乳腺病变分割的准确性，尤其是在数据欠采样或嘈杂的情况下。这种新颖的方法在公开的DCE-MRI数据集上进行了测试，在加速采样条件下，其性能优于传统的图像空间基线。研究表明，在完全采样的情况下，将频域滤波与图像域定位相结合可以提高分割的鲁棒性，而不会牺牲准确性。 AI

影响 通过在具有挑战性的数据条件下提高分割鲁棒性，增强了医学影像的诊断准确性。

排序理由 该集群包含一篇详细介绍医学图像分析新方法的学术论文。

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 →

深度学习提高了MRI乳腺病变分割的准确性

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该集群包含一篇详细介绍医学图像分析新方法的学术论文。
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2 independent sources
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paper, model release
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134 days old
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Lukas T. Rotkopf, Marco Schlimbach, Julius C. Holzschuh, Heinz-Peter Schlemmer, Jens Kleesiek, Moritz Rempe ·

    k空间感知深度学习可提高MRI欠采样下乳腺病变分割的鲁棒性

    arXiv:2605.22327v1 Announce Type: new Abstract: Purpose: To assess whether breast lesion segmentation can be learned directly from acquired MRI k-space, and whether doing so improves robustness when data are accelerated or noisy. Materials and Methods: This retrospective study us…

  2. arXiv cs.CV TIER_1 English(EN) · Moritz Rempe ·

    k空间感知深度学习可提高MRI欠采样下乳腺病变分割的鲁棒性

    Purpose: To assess whether breast lesion segmentation can be learned directly from acquired MRI k-space, and whether doing so improves robustness when data are accelerated or noisy. Materials and Methods: This retrospective study used public breast dynamic contrast-enhanced MRI (…

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