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High-throughput adaptive sampling for whole-slide histopathology image analysis

机译:高通量自适应采样用于全幻灯片组织病理学图像分析

摘要

Methods, apparatus, and other embodiments associated with classifying a region of tissue represented in a digitized whole slide image (WSI) using iterative gradient-based quasi-Monte Carlo (QMC) sampling. One example apparatus includes an image acquisition circuit that acquires a WSI of a region of tissue demonstrating cancerous pathology, an adaptive sampling circuit that selects a subset of tiles from the WSI using an iterative QMC Sobol sequence sampling approach, an invasiveness circuit that determines a probability of a presence of invasive pathology in a member of the subset of tiles, a probability map circuit that generates an invasiveness probability map based on the probability, a probability gradient circuit that generates a gradient image based on the invasiveness probability map, and a classification circuit that classifies the region of tissue based on the probability map. A prognosis or treatment plan may be provided based on the classification of the WSI.
机译:与使用基于迭代梯度的准蒙特卡罗(QMC)采样对数字化的整个幻灯片图像(WSI)中表示的组织区域进行分类相关的方法,设备和其他实施例。一个示例装置包括:图像采集电路,其获取表现出癌性病理的组织区域的WSI;自适应采样电路,其使用迭代QMC Sobol序列采样方法从WSI中选择瓦片的子集;侵入性电路,其确定概率。瓦片子集的成员中是否存在侵袭性病理,基于概率生成侵略性概率图的概率图电路,基于侵略性概率图生成梯度图像的概率梯度电路以及分类电路根据概率图对组织区域进行分类。可以基于WSI的分类提供预后或治疗计划。

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