Efficient Classification of Very High Resolution Histopathological Images
Efficient Classification of Very High Resolution Histopathological Images
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DOI：
10.1109/bibm55620.2022.9994942
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Mohammad Iqbal Nouyed;Gianfranco Doretto;D. Adjeroh
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文献类型：
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作者：
Mohammad Iqbal Nouyed;Gianfranco Doretto;D. Adjeroh
多年来，深度学习方法在各种图像理解任务中显示出显着的改进。然而，高分辨率图像的分析仍然是一个主要的挑战。除了这些图像需要大量的计算资源外，大的图像尺寸使得难以提取重要任务所需的有效上下文信息，例如此类图像的分类、分割或聚类。在这项工作中，我们使用一种新的判别补丁选择方法来解决高分辨率图像分类的挑战。我们将我们的补丁选择方法嵌入到一个新的分类框架中，支持不同预训练学习模型的潜在使用。我们展示了高分辨率图像数据集的结果，即癌症肿瘤的十亿像素整片组织图像。我们在此数据集上使用与最先进方法的比较分析来展示所提出方法的性能。
Over the years, deep learning approaches have shown significant improvement in various image understanding tasks. However, analysis of high resolution images still remains a major challenge. Apart from the huge computational resources required for such images, the large image sizes make it difficult to extract effective contextual information needed for important tasks, such as classification, segmentation, or clustering of such images. In this work, we address the challenge of high resolution image classification u sing a new discriminative patch selection approach. We embed our patch selection approach inside a novel classification framework, supporting potential use of different pre-trained learning models. We show results on a high resolution image dataset, namely, gigapixel whole slide tissue images for cancer tumors. We demonstrate the performance of the proposed approaches using comparative analysis with state-of-the art methods on this dataset.