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METHOD AND SYSTEM FOR ASSISTING PATHOLOGIST IDENTIFICATION OF TUMOR CELLS IN MAGNIFIED TISSUE IMAGES

机译:放大的组织图像中肿瘤细胞的病理鉴定的方法和系统

摘要

A method, system and machine for assisting a pathologist in identifying the presence of tumor cells in lymph node tissue is disclosed. The digital image of lymph node tissue at a first magnification (e.g., 40×) is subdivided into a multitude of rectangular “patches.” A likelihood of malignancy score is then determined for each of the patches. The score is obtained by analyzing pixel data from the patch (e.g., pixel data centered on and including the patch) using a computer system programmed as an ensemble of deep neural network pattern recognizers, each operating on different magnification levels of the patch. A representation or “heatmap” of the slide is generated. Each of the patches is assigned a color or grayscale value in accordance with (1) the likelihood of malignancy score assigned to the patch by the combined outputs of the ensemble of deep neural network pattern recognizers and (2) a code which assigns distinct colors (or grayscale values) to different values of likelihood of malignancy scores assigned to the patches.
机译:公开了一种用于协助病理学家识别淋巴结组织中肿瘤细胞的存在的方法,系统和机器。第一次放大(例如40倍)的淋巴结组织的数字图像可细分为多个矩形“斑块”。然后确定每个斑块的恶性评分的可能性。通过使用被编程为深度神经网络模式识别器的集合的计算机系统来分析来自补丁的像素数据(例如,以补丁为中心并包括补丁的像素数据)来获得分数,每个计算机都在补丁的不同放大率级别上进行操作。生成幻灯片的表示或“热图”。根据(1)通过深度神经网络模式识别器集合的组合输出分配给补丁的恶性评分的可能性,以及(2)分配不同颜色的代码(或灰度值)分配给贴片的恶性评分可能性的不同值。

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