Abstract
Estimating over-amplification of human epidermal growth factor receptor 2 (HER2) on invasive breast cancer (BC) is regarded as a significant predictive and prognostic marker. We propose a novel deep reinforcement learning (DRL) based model that treats immunohistochemical (IHC) scoring of HER2 as a sequential learning task. For a given image tile sampled from multi-resolution giga-pixel whole slide image (WSI), the model learns to sequentially identify some of the diagnostically relevant regions of interest (ROIs) by following a parameterized policy. The selected ROIs are processed by recurrent and residual convolution networks to learn the discriminative features for different HER2 scores and predict the next location, without requiring to process all the sub-image patches of a given tile for predicting the HER2 score, mimicking the histopathologist who would not usually analyze every part of the slide at the highest magnification. The proposed model incorporates a task-specific regularization term and inhibition of return mechanism to prevent the model from revisiting the previously attended locations. We evaluated our model on two IHC datasets: a publicly available dataset from the HER2 scoring challenge contest and another dataset consisting of WSIs of gastroenteropancreatic neuroendocrine tumor sections stained with Glo1 marker. We demonstrate that the proposed model outperforms other methods based on state-of-the-art deep convolutional networks. To the best of our knowledge, this is the first study using DRL for IHC scoring and could potentially lead to wider use of DRL in the domain of computational pathology reducing the computational burden of the analysis of large multigigapixel histology images.
Abstract (translated)
评估人表皮生长因子受体2（her2）对侵袭性乳腺癌（bc）的过度扩增是一个重要的预测和预后标志。我们提出了一种新的基于深度强化学习（DRL）的模型，将HER2的免疫组织化学（IHC）评分作为一个连续的学习任务。对于从多分辨率千兆像素整张幻灯片图像（WSI）中采样的给定图像块，该模型学习通过遵循参数化策略来依次识别一些诊断相关的感兴趣区域（ROI）。所选ROI通过反复和剩余卷积网络进行处理，以了解不同her2评分的识别特征并预测下一个位置，而无需处理给定图块的所有子图像补丁来预测her2评分，模仿通常不会分析最大放大倍率的载玻片。该模型包括一个特定于任务的正则化项和返回抑制机制，以防止该模型重新访问以前关注的位置。我们在两个IHC数据集上评估了我们的模型：一个来自Her2评分挑战赛的公开数据集，另一个数据集由用Glo1标记物染色的胃肠胰腺神经内分泌肿瘤切片的WSIS组成。我们证明了该模型优于其他基于最先进的深卷积网络的方法。据我们所知，这是第一个使用DRL进行IHC评分的研究，可能会导致DRL在计算病理学领域的广泛应用，从而减少分析大型数十亿像素组织学图像的计算负担。
URL
https://arxiv.org/abs/1903.10762
