论文标题
基于深度学习的NAS评分和纤维化阶段从CT和病理数据预测
Deep Learning based NAS Score and Fibrosis Stage Prediction from CT and Pathology Data
论文作者
论文摘要
在世界人口中，非酒精性脂肪肝病（NAFLD）变得越来越普遍。在正确的时间没有诊断的情况下，NAFLD可以导致非酒精性脂肪性肝炎（NASH）和随后的肝损害。 NAFLD的诊断和治疗取决于NAFLD活性评分（NAS）和肝纤维化阶段，病理学家通常从肝活检中评估。在这项工作中，我们提出了一种新的方法，可以从CT数据中自动预测NAS评分和纤维化阶段，而CT数据与肝活检相比，无创和便宜。我们还提出了一种将CT和H \＆E染色病理数据中信息组合在一起的方法，以提高NAS评分和纤维化阶段预测的性能，当时两种类型的数据可用。这是有助于病理学家参与计算机辅助诊断过程的重要价值。 30名患者数据集的实验说明了我们方法的有效性。
Non-Alcoholic Fatty Liver Disease (NAFLD) is becoming increasingly prevalent in the world population. Without diagnosis at the right time, NAFLD can lead to non-alcoholic steatohepatitis (NASH) and subsequent liver damage. The diagnosis and treatment of NAFLD depend on the NAFLD activity score (NAS) and the liver fibrosis stage, which are usually evaluated from liver biopsies by pathologists. In this work, we propose a novel method to automatically predict NAS score and fibrosis stage from CT data that is non-invasive and inexpensive to obtain compared with liver biopsy. We also present a method to combine the information from CT and H\&E stained pathology data to improve the performance of NAS score and fibrosis stage prediction, when both types of data are available. This is of great value to assist the pathologists in computer-aided diagnosis process. Experiments on a 30-patient dataset illustrate the effectiveness of our method.
