In this paper, we present a novel method to automatically classify medical images that learns and leverages weak causal signals in the image. Our framework consists of a convolutional neural network backbone and a causality-extractor module that extracts cause-effect relationships between feature maps that can inform the model on the appearance of a feature in one place of the image, given the presence of another feature within some other place of the image. To evaluate the effectiveness of our approach in low-data scenarios, we train our causality-driven architecture in a One-shot learning scheme, where we propose a new meta-learning procedure entailing meta-training and meta-testing tasks that are designed using related classes but at different levels of granularity. We conduct binary and multi-class classification experiments on a publicly available dataset of prostate MRI images. To validate the effectiveness of the proposed causality-driven module, we perform an ablation study and conduct qualitative assessments using class activation maps to highlight regions strongly influencing the network's decision-making process. Our findings show that causal relationships among features play a crucial role in enhancing the model's ability to discern relevant information and yielding more reliable and interpretable predictions. This would make it a promising approach for medical image classification tasks.

本研究介绍了一种新颖的方法，通过学习和利用图像中的弱因果信号来自动分类医学图像。我们的框架由一个卷积神经网络主体和一个因果提取器模块组成，该模块提取特征图之间的因果关系，可以向模型提供关于图像中某个位置的特征出现的信息，当图像的另一个位置存在其他特征时。我们通过对前列腺MRI图像的公开数据集进行二元和多类分类实验证明了我们的方法在低数据场景下的有效性，并通过消融研究和定性评估使用类激活图来突出强烈影响网络决策过程的区域，验证了所提出的因果驱动模块的有效性。研究结果表明，特征之间的因果关系在提高模型识别相关信息和产生更可靠、可解释的预测方面起着关键作用，这使其成为医学图像分类任务中一个有希望的方法。

基于因果关系驱动的磁共振成像前列腺癌分级的单次学习