利用医学图像中的因果信号：一项带有实证结果的初步研究
Exploiting Causality Signals in Medical Images: A Pilot Study with Empirical Results
AI总结：
提出一种结合卷积神经网络主干和因果因子提取器模块的新方法，通过从医学图像中提取弱因果信号来增强特征图，在前列腺MRI和乳腺癌组织病理学数据集上验证了其能有效提升分类性能与预测鲁棒性。
摘要：
我们提出了一种新技术，通过神经网络直接从图像中发现并利用弱因果信号以进行分类。这种方式建模了图像某一部分特征的存在如何影响另一部分特征的外观。我们的方法由一个卷积神经网络主干和一个因果因子提取器模块组成，该模块计算权重以根据每个特征图在场景中的因果影响来增强其表示。我们开发了不同的架构变体，并在两个公开数据集（前列腺MRI图像和乳腺癌组织病理学切片）上针对癌症诊断对所有模型进行了实证评估。我们研究了该模块在完全监督和少样本学习中的有效性，评估了其与现有基于注意力的解决方案的结合，进行了消融研究，并通过类激活图探究了模型的可解释性。研究结果表明，我们的轻量级模块提取了有意义的信息并改善了整体分类效果，同时产生了更鲁棒的预测，这些预测聚焦于图像的相关部分。这在医学成像中至关重要，因为准确可靠的分类对于有效的诊断和治疗规划是必不可少的。
英文摘要：
We present a novel technique to discover and exploit weak causal signals directly from images via neural networks for classification purposes. This way, we model how the presence of a feature in one part of the image affects the appearance of another feature in a different part of the image. Our method consists of a convolutional neural network backbone and a causality-factors extractor module, which computes weights to enhance each feature map according to its causal influence in the scene. We develop different architecture variants and empirically evaluate all the models on two public datasets of prostate MRI images and breast histopathology slides for cancer diagnosis. We study the effectiveness of our module both in fully-supervised and few-shot learning, we assess its addition to existing attention-based solutions, we conduct ablation studies, and investigate the explainability of our models via class activation maps. Our findings show that our lightweight block extracts meaningful information and improves the overall classification, together with producing more robust predictions that focus on relevant parts of the image. That is crucial in medical imaging, where accurate and reliable classifications are essential for effective diagnosis and treatment planning.
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