论文标题
从组织病理学贴片的标题推理
Inference of captions from histopathological patches
论文作者
论文摘要
在过去的几年中，计算组织病理学取得了长足的进步，逐渐接近临床采用。利益的一个领域将是从H \＆e染色的全幻灯片图像中自动生成诊断报告，这将进一步提高病理学家常规诊断工作流的效率。在这项研究中，我们编制了胃腺癌内窥镜活检标本的组织病理学标题的数据集（PatchgastricAdc22），我们从诊断报告中提取了该标题，并与从相关的整个幻灯片图像中提取的斑块配对。该数据集包含各种胃腺癌亚型。我们训练了基于基准注意的模型，以预测从斑块中提取的特征并获得有希望的结果的标题。我们将公开可用的262K补丁的标题数据集。
Computational histopathology has made significant strides in the past few years, slowly getting closer to clinical adoption. One area of benefit would be the automatic generation of diagnostic reports from H\&E-stained whole slide images which would further increase the efficiency of the pathologists' routine diagnostic workflows. In this study, we compiled a dataset (PatchGastricADC22) of histopathological captions of stomach adenocarcinoma endoscopic biopsy specimens, which we extracted from diagnostic reports and paired with patches extracted from the associated whole slide images. The dataset contains a variety of gastric adenocarcinoma subtypes. We trained a baseline attention-based model to predict the captions from features extracted from the patches and obtained promising results. We make the captioned dataset of 262K patches publicly available.
