具有共享特征的分析-综合模型学习：一种新的框架 用于组织病理学图像分类

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摘要翻译：
自动组织病理学图像分析为包括癌症在内的几种疾病的早期诊断提供了令人兴奋的机会。然而，仍有严峻的实际挑战：1.）从这样的图像中区分疾病和健康类别的鉴别特征并不容易明显，和2。）不同的类别，例如健康与疾病阶段，继续共享几个几何特征。提出了一种新的基于共享特征的分析-综合模型学习算法(ALSF)来更有效地对此类图像进行分类。在ALSF中，引入了一种联合分析和综合学习模型，用于同时学习分类器和特征提取器。这样可以大大减少基于块级的图像分类的计算量。至关重要的是，我们在这个框架中集成了一个低秩共享字典和一个共享分析算子的学习，它更准确地表示来自不同类别的组织病理学图像的相似性和差异性。ALSF是在两个具有挑战性的数据库中评估的：（1）宾夕法尼亚州立大学动物诊断实验室(ADL)提供的肾组织图像和（2）来自癌症基因组图谱(TCGA)数据库的脑肿瘤图像。实验结果证实ALSF可以提供比现有的替代方案更好的好处。
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英文标题：
《Analysis-synthesis model learning with shared features: a new framework
  for histopathological image classification》
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作者：
Xuelu Li and Vishal Monga and U. K. Arvind Rao
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最新提交年份：
2017
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  Automated histopathological image analysis offers exciting opportunities for the early diagnosis of several medical conditions including cancer. There are however stiff practical challenges: 1.) discriminative features from such images for separating diseased vs. healthy classes are not readily apparent, and 2.) distinct classes, e.g. healthy vs. stages of disease continue to share several geometric features. We propose a novel Analysis-synthesis model Learning with Shared Features algorithm (ALSF) for classifying such images more effectively. In ALSF, a joint analysis and synthesis learning model is introduced to learn the classifier and the feature extractor at the same time. In this way, the computation load in patch-level based image classification can be much reduced. Crucially, we integrate into this framework the learning of a low rank shared dictionary and a shared analysis operator, which more accurately represents both similarities and differences in histopathological images from distinct classes. ALSF is evaluated on two challenging databases: (1) kidney tissue images provided by the Animal Diagnosis Lab (ADL) at the Pennsylvania State University and (2) brain tumor images from The Cancer Genome Atlas (TCGA) database. Experimental results confirm that ALSF can offer benefits over state of the art alternatives.
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PDF链接：
https://arxiv.org/pdf/1712.08227

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关键词：病理学 Pathological Similarities Segmentation Applications 类别 诊断 特征 images 综合