Multi-feature based Benchmark for Cervical Dysplasia Classification Evaluation.
Multi-feature based Benchmark for Cervical Dysplasia Classification Evaluation.
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DOI：
10.1016/j.patcog.2016.09.027
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发表时间：
2017-03
影响因子：
8
通讯作者：
Huang X
中科院分区：
文献类型：
--
作者：
Xu T;Zhang H;Xin C;Kim E;Long LR;Xue Z;Antani S;Huang X
宫颈癌是世界范围内女性最常见的癌症之一。这种疾病造成的大多数死亡发生在世界上较不发达地区。在这项工作中，我们引入了一个新的图像数据集沿着与专家注释的诊断评估基于图像的宫颈疾病分类算法。大量Cervigram®图像选自美国国家癌症研究所提供的数据库。对于每幅图像，我们提取三个互补的金字塔特征：L*A*B* 颜色空间中的金字塔直方图（PLAB）、方向梯度金字塔直方图（PHOG）和局部二值模式金字塔直方图（PLBP）。除了手工制作的金字塔特征之外，我们还研究了卷积神经网络（CNN）特征用于宫颈疾病分类的性能。我们的实验结果证明了我们的手工制作和我们的深度功能的有效性。我们打算发布这个多特征数据集，我们使用七个经典分类器进行的广泛评估可以作为基线。
Cervical cancer is one of the most common types of cancer in women worldwide. Most deaths due to the disease occur in less developed areas of the world. In this work, we introduce a new image dataset along with expert annotated diagnoses for evaluating image-based cervical disease classification algorithms. A large number of Cervigram® images are selected from a database provided by the US National Cancer Institute. For each image, we extract three complementary pyramid features: Pyramid histogram in L*A*B* color space (PLAB), Pyramid Histogram of Oriented Gradients (PHOG), and Pyramid histogram of Local Binary Patterns (PLBP). Other than hand-crafted pyramid features, we investigate the performance of convolutional neural network (CNN) features for cervical disease classification. Our experimental results demonstrate the effectiveness of both our hand-crafted and our deep features. We intend to release this multi-feature dataset and our extensive evaluations using seven classic classifiers can serve as the baseline.