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English(EN) A Multi-Dataset Benchmark of Multiple Instance Learning for 3D Neuroimage Classification

简单的MIL在3D神经影像分类中可媲美复杂模型

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员发布了一项基准测试，将多种多实例学习（MIL）方法与3D CNN和ViT在3D神经影像分类任务上进行了比较。研究发现，一种简单的均值池化MIL方法，即使没有注意力机制，在多项任务上的表现也与更复杂的方法相当或更好。这种基线MIL方法训练速度也显著更快，使其成为计算资源有限的从业者的可行选择。 AI

影响 为3D神经影像分类中高效神经网络的选择提供了基准，有助于资源受限的从业者。

排序理由 学术论文，比较了特定任务上不同的机器学习架构。

在 arXiv cs.LG 阅读 →

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

简单的MIL在3D神经影像分类中可媲美复杂模型

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Ethan Harvey, Dennis Johan Loevlie, Amir Ali Satani, Wansu Chen, David M. Kent, Michael C. Hughes ·

    用于3D神经影像分类的多实例学习多数据集基准测试

    arXiv:2604.26807v1 Announce Type: new Abstract: Despite being resource-intensive to train, 3D convolutional neural networks (CNNs) have been the standard approach to classify CT and MRI scans. Recent work suggests that deep multiple instance learning (MIL) may be a more efficient…

  2. arXiv cs.LG TIER_1 English(EN) · Michael C. Hughes ·

    用于3D神经影像分类的多实例学习多数据集基准测试

    Despite being resource-intensive to train, 3D convolutional neural networks (CNNs) have been the standard approach to classify CT and MRI scans. Recent work suggests that deep multiple instance learning (MIL) may be a more efficient alternative for 3D brain scans, especially when…

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