Breast cancer is one of the most common cancers in women, with more than 1,300,000 cases and 450,000 deaths each year worldwide. In this context, recent studies showed that early breast cancer detection, along with suitable treatment, could significantly reduce breast cancer death rates in the long term. X-ray mammography is still the instrument of choice in breast cancer screening. In this context, the false-positive and false-negative rates commonly achieved by radiologists are extremely arduous to estimate and control although some authors have estimated figures of up to 20% of total diagnoses or more. The introduction of novel artificial intelligence (AI) technologies applied to the diagnosis and, possibly, prognosis of breast cancer could revolutionize the current status of the management of the breast cancer patient by assisting the radiologist in clinical image interpretation. Lately, a breakthrough in the AI field has been brought about by the introduction of deep learning techniques in general and of convolutional neural networks in particular. Such techniques require no a priori feature space definition from the operator and are able to achieve classification performances which can even surpass human experts. In this paper, we design and validate an ad hoc CNN architecture specialized in breast lesion classification from imaging data only. We explore a total of 260 model architectures in a train-validation-test split in order to propose a model selection criterion which can pose the emphasis on reducing false negatives while still retaining acceptable accuracy. We achieve an area under the receiver operatic characteristics curve of 0.785 (accuracy 71.19%) on the test set, demonstrating how an ad hoc random initialization architecture can and should be fine tuned to a specific problem, especially in biomedical applications.

中文翻译：

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用于在乳腺X射线照片图像中区分恶性乳腺癌病变的临时随机初始化深度神经网络体系结构。

乳腺癌是女性中最常见的癌症之一，全世界每年有130万例以上的病例和45万例死亡。在这种情况下，最近的研究表明，早期发现乳腺癌以及采取适当的治疗措施可以长期显着降低乳腺癌的死亡率。X射线乳腺摄影仍然是乳腺癌筛查的首选工具。在这种情况下，放射线医生通常达到的假阳性和假阴性率很难估计和控制，尽管有些作者估计占诊断总数的20％或更多。引入了用于诊断的新型人工智能（AI）技术，并可能 通过协助放射科医生进行临床图像解释，乳腺癌的预后可能会改变乳腺癌患者的治疗现状。最近，通过普遍引入深度学习技术，特别是卷积神经网络，在AI领域取得了突破。这样的技术不需要操作者的先验特征空间定义，并且能够实现甚至超过人类专家的分类性能。在本文中，我们仅根据成像数据设计并验证了专门用于乳腺病变分类的特设CNN架构。为了提出一个模型选择标准，我们将在火车验证测试中探索总共260个模型体系结构，以提出一种模型选择标准，该标准可以强调减少假阴性，同时仍保持可接受的准确性。在测试集上，我们在接收器操作特性曲线下获得了0.785（精度为71.19％）的面积，这说明了临时随机初始化体系结构可以并且应该针对特定问题进行微调，尤其是在生物医学应用中。