SBILab, Department of ECE, IIIT-Delhi, New Delhi, 110020, India.
SBILab, Department of ECE, IIIT-Delhi, New Delhi, 110020, India.
Med Image Anal. 2020 Apr;61:101661. doi: 10.1016/j.media.2020.101661. Epub 2020 Feb 4.
Acute lymphoblastic leukemia (ALL) is a pervasive pediatric white blood cell cancer across the globe. With the popularity of convolutional neural networks (CNNs), computer-aided diagnosis of cancer has attracted considerable attention. Such tools are easily deployable and are cost-effective. Hence, these can enable extensive coverage of cancer diagnostic facilities. However, the development of such a tool for ALL cancer was challenging so far due to the non-availability of a large training dataset. The visual similarity between the malignant and normal cells adds to the complexity of the problem. This paper discusses the recent release of a large dataset and presents a novel deep learning architecture for the classification of cell images of ALL cancer. The proposed architecture, namely, SDCT-AuxNet is a 2-module framework that utilizes a compact CNN as the main classifier in one module and a Kernel SVM as the auxiliary classifier in the other one. While CNN classifier uses features through bilinear-pooling, spectral-averaged features are used by the auxiliary classifier. Further, this CNN is trained on the stain deconvolved quantity images in the optical density domain instead of the conventional RGB images. A novel test strategy is proposed that exploits both the classifiers for decision making using the confidence scores of their predicted class labels. Elaborate experiments have been carried out on our recently released public dataset of 15114 images of ALL cancer and healthy cells to establish the validity of the proposed methodology that is also robust to subject-level variability. A weighted F1 score of 94.8% is obtained that is best so far on this challenging dataset.
急性淋巴细胞白血病（ALL）是一种普遍存在于全球儿童中的白细胞癌症。随着卷积神经网络（CNNs）的普及，癌症的计算机辅助诊断引起了相当大的关注。这些工具易于部署且具有成本效益。因此，它们可以实现癌症诊断设施的广泛覆盖。然而，到目前为止，由于缺乏大型训练数据集，开发用于 ALL 癌症的此类工具具有挑战性。恶性和正常细胞之间的视觉相似性增加了问题的复杂性。本文讨论了最近发布的一个大型数据集，并提出了一种用于 ALL 癌症细胞图像分类的新型深度学习架构。所提出的架构，即 SDCT-AuxNet，是一个 2 模块框架，在一个模块中使用紧凑的 CNN 作为主分类器，在另一个模块中使用核 SVM 作为辅助分类器。虽然 CNN 分类器通过双线性池化使用特征，但辅助分类器使用平均谱特征。此外，该 CNN 是在光学密度域中对去卷积数量图像进行训练的，而不是传统的 RGB 图像。提出了一种新的测试策略，该策略利用两个分类器的置信度分数来进行决策，以利用其预测的类标签。在我们最近发布的包含 15114 张 ALL 癌症和健康细胞图像的公共数据集上进行了详细的实验，以验证该方法的有效性，该方法对个体水平的变化也具有鲁棒性。在这个具有挑战性的数据集上，获得了 94.8%的加权 F1 得分，这是迄今为止的最佳得分。
