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 developed different architecture variants and empirically evaluated all of our models on two public datasets of prostate MRI images and breast histopathology slides for cancer diagnosis. To confirm our quantitative results, 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.

翻译：我们提出一种新颖技术，通过神经网络直接从图像中发现并利用弱因果信号进行分类。该方法通过建模图像某区域中特征的存在如何影响另一区域特征的表现形式。我们的方法包含卷积神经网络主干网络与因果因子提取模块，该模块根据各特征图在场景中的因果影响力计算权重以增强特征。我们开发了多种架构变体，并在两个公共数据集（前列腺MRI图像与乳腺组织病理切片）上对癌症诊断任务进行了实证评估。为验证定量结果，我们开展消融实验，并通过类激活图谱研究模型的可解释性。实验表明，轻量级模块能提取有意义信息并提升整体分类性能，同时生成更稳健的预测结果，聚焦图像相关区域。这在医学影像领域至关重要——准确可靠的分类是实现有效诊断与治疗规划的关键。