Breast cancer is the most widespread neoplasm among women and early detection of this disease is critical. Deep learning techniques have become of great interest to improve diagnostic performance. Nonetheless, discriminating between malignant and benign masses from whole mammograms remains challenging due to them being almost identical to an untrained eye and the region of interest (ROI) occupying a minuscule portion of the entire image. In this paper, we propose a framework, parameterized hypercomplex attention maps (PHAM), to overcome these problems. Specifically, we deploy an augmentation step based on computing attention maps. Then, the attention maps are used to condition the classification step by constructing a multi-dimensional input comprised of the original breast cancer image and the corresponding attention map. In this step, a parameterized hypercomplex neural network (PHNN) is employed to perform breast cancer classification. The framework offers two main advantages. First, attention maps provide critical information regarding the ROI and allow the neural model to concentrate on it. Second, the hypercomplex architecture has the ability to model local relations between input dimensions thanks to hypercomplex algebra rules, thus properly exploiting the information provided by the attention map. We demonstrate the efficacy of the proposed framework on both mammography images as well as histopathological ones, surpassing attention-based state-of-the-art networks and the real-valued counterpart of our method. The code of our work is available at https://github.com/elelo22/AttentionBCS.

翻译：乳腺癌是女性中最常见的肿瘤，早期发现对该疾病的诊断至关重要。深度学习方法在提升诊断性能方面引起了广泛关注。然而，从完整乳腺X光片中区分良性与恶性肿块仍然具有挑战性，原因是它们对未受过训练的眼睛而言几乎相同，且感兴趣区域（ROI）仅占整个图像的极小部分。本文提出了一种名为参数化超复数注意力图（PHAM）的框架来解决这些问题。具体而言，我们基于计算注意力图部署了一个增强步骤。随后，通过构建由原始乳腺癌图像及其对应注意力图组成的多维输入，利用注意力图来约束分类步骤。在此步骤中，采用参数化超复数神经网络（PHNN）进行乳腺癌分类。该框架具备两大优势：首先，注意力图提供了关于ROI的关键信息，使神经模型能够聚焦于该区域；其次，超复数架构凭借超复数代数规则，具备建模输入维度间局部关系的能力，从而有效利用注意力图提供的信息。我们在乳腺X光图像和组织病理学图像上均验证了所提框架的有效性，其性能超越了基于注意力的现有先进网络以及我们方法的实值对应版本。本文代码可在https://github.com/elelo22/AttentionBCS获取。