We present a new method for automatically classifying medical images that uses weak causal signals in the scene to 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 two components: a convolutional neural network backbone and a causality-factors extractor module. The latter computes weights for the feature maps to enhance each feature map according to its causal influence in the image's scene. We can modify the functioning of the causality module by using two external signals, thus obtaining different variants of our method. We evaluate our method on a public dataset of prostate MRI images for prostate cancer diagnosis, using quantitative experiments, qualitative assessment, and ablation studies. Our results show that our method improves classification performance and produces more robust predictions, focusing on relevant parts of the image. That is especially important in medical imaging, where accurate and reliable classifications are essential for effective diagnosis and treatment planning.

翻译：我们提出了一种新的医学图像自动分类方法，该方法利用场景中的弱因果信号，对图像某一部分特征的存在如何影响另一部分特征的表现进行建模。该方法由两个组件构成：卷积神经网络骨干网络与因果因子提取模块。后者为特征图计算权重，根据每个特征图在图像场景中的因果影响对其进行增强。通过使用两个外部信号，我们可调整因果模块的功能，从而获得该方法的不同变体。我们在用于前列腺癌诊断的前列腺MRI图像公共数据集上进行了定量实验、定性评估及消融研究来评估该方法。结果表明，我们的方法提升了分类性能，生成了更稳健的预测结果，并聚焦于图像的相关区域。这一点在医学影像中尤为重要，因为准确可靠的分类对于有效诊断和治疗规划至关重要。