Tumor Mutation Burden(TMB) is a quantifiable clinical indicator that can be used to predict the responses to immunotherapy of a range of tumors. However, the current DNA sequencing-based TMB measurement method represented by Whole Exome Sequencing (WES) is expensive and time-consuming, which limits its utilization in clinical practice. In this paper, we design a method through deep learning in order to predict TMB from available H&E stained whole slide images of gastrointestinal cancer. Experimental results demonstrate that our approach is capable of distinguishing high and low TMB with an AUC higher than 0.75. We further performed post-processing to improve the accuracy on both test sets to above 0.7 (0.71 accuracy for TMB-STAD and 0.77 accuracy for TMB-COAD-DX). Furthermore, the predicted low and high TMB patients with gastric and colon cancer have different survival rates, with p values of 0.348 and 0.8113, respectively, which indicates that our study is potentially helpful for practical treatment.

肿瘤突变负担（TMB）是一种可量化的临床指标，可用于预测一系列肿瘤对免疫治疗的反应。但是，以全外显子组测序（WES）为代表的当前基于DNA测序的TMB测量方法昂贵且耗时，这限制了其在临床实践中的利用。在本文中，我们设计了一种通过深度学习的方法，以便从可用的H＆E染色的胃肠道肿瘤全片图像中预测TMB。实验结果表明，我们的方法能够区分ATC高于0.75的高TMB和低TMB。我们进一步进行了后处理，以将两个测试集的准确性提高到0.7以上（TMB-STAD的准确性为0.71，TMB-COAD-DX的准确性为0.77）。此外，