Mammographic image analysis requires accurate localisation of salient mammographic masses. In mammographic computer-aided diagnosis, mass or Region of Interest (ROI) is often marked by physicians and features are extracted from the marked ROI. In this paper, we present a novel mammographic mass localisation framework, based on the maximal class activations of the stacked auto-encoders. We hypothesize that the image regions activating abnormal classes in mammographic images will be the breast masses which causes the anomaly. The experiment is conducted using randomly selected 200 mammographic images (100 normal and 100 abnormal) from IRMA mammographic dataset. Abnormal mass regions marked by an expert radiologist are used as the ground truth. The proposed method outperforms existing Deep Convolutional Neural Network (DCNN) based techniques in terms of salient region detection accuracy. The proposed greedy backtracking method is more efficient and does not require a vast number of labelled training images as in DCNN based method. Such automatic localisation method will assist physicians to make accurate decisions on biopsy recommendations and treatment evaluations.

翻译：乳腺钼靶图像分析需要对显著性乳腺肿块进行精确定位。在乳腺钼靶计算机辅助诊断中，肿块或感兴趣区域通常由医师标注，并从标注区域中提取特征。本文提出一种基于栈式自编码器最大类别激活值的新型乳腺钼靶肿块定位框架。我们假设在乳腺钼靶图像中激活异常类别的图像区域即为引起异常的乳腺肿块。实验采用从IRMA乳腺钼靶数据集中随机选取的200张图像（100张正常、100张异常），以放射科专家标注的异常肿块区域作为金标准。所提方法在显著区域检测精度上优于现有基于深度卷积神经网络的技术。本文提出的贪婪回溯方法效率更高，且无需像深度卷积神经网络方法那样依赖大量标注训练图像。这种自动定位方法将辅助医师在活检建议与疗效评估中做出准确决策。