Deep Learning-Based Pixel-Wise Lesion Segmentation on Oral Squamous Cell Carcinoma Images
Deep Learning-Based Pixel-Wise Lesion Segmentation on Oral Squamous Cell Carcinoma Images
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DOI：
10.3390/app10228285
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发表时间：
2020-11-01
影响因子：
2.7
通讯作者：
Merolla, Francesco
中科院分区：
文献类型：
--
作者：
Martino, Francesco;Bloisi, Domenico D.;Merolla, Francesco
口腔鳞状细胞癌是最常见的口腔癌。在本文中，我们对四种不同的基于深度学习的逐像素方法进行了性能分析，用于口腔癌图像的病变分割。两个不同的图像数据集，一个用于训练，另一个用于测试，用于生成和评估用于分割图像的模型，从而允许评估所考虑的深度网络架构的泛化能力。这项工作的一个重要贡献是创建了口腔癌注释（ORCA）数据集，其中包含来自著名的癌症基因组图谱（TCGA）数据集的真实数据。
Oral squamous cell carcinoma is the most common oral cancer. In this paper, we present a performance analysis of four different deep learning-based pixel-wise methods for lesion segmentation on oral carcinoma images. Two diverse image datasets, one for training and another one for testing, are used to generate and evaluate the models used for segmenting the images, thus allowing to assess the generalization capability of the considered deep network architectures. An important contribution of this work is the creation of the Oral Cancer Annotated (ORCA) dataset, containing ground-truth data derived from the well-known Cancer Genome Atlas (TCGA) dataset.