Traditionally, pathological analysis and diagnosis are performed by manually eyeballing glass slide specimens under a microscope by an expert. The whole slide image is the digital specimen produced from the glass slide. Whole slide image enabled specimens to be observed on a computer screen and led to computational pathology where computer vision and artificial intelligence are utilized for automated analysis and diagnosis. With the current computational advancement, the entire whole slide image can be analyzed autonomously without human supervision. However, the analysis could fail or lead to wrong diagnosis if the whole slide image is affected by tissue artifacts such as tissue fold or air bubbles depending on the severity. Existing artifact detection methods rely on experts for severity assessment to eliminate artifact affected regions from the analysis. This process is time consuming, exhausting and undermines the goal of automated analysis or removal of artifacts without evaluating their severity, which could result in the loss of diagnostically important data. Therefore, it is necessary to detect artifacts and then assess their severity automatically. In this paper, we propose a system that incorporates severity evaluation with artifact detection utilizing convolutional neural networks. The proposed system uses DoubleUNet to segment artifacts and an ensemble network of six fine tuned convolutional neural network models to determine severity. This method outperformed current state of the art in accuracy by 9 percent for artifact segmentation and achieved a strong correlation of 97 percent with the evaluation of pathologists for severity assessment. The robustness of the system was demonstrated using our proposed heterogeneous dataset and practical usability was ensured by integrating it with an automated analysis system.

翻译：传统上，病理分析与诊断由专家在显微镜下人工目测玻璃切片标本完成。全切片图像是由玻璃切片生成的数字化标本，其出现使标本得以在计算机屏幕上观察，并催生了利用计算机视觉与人工智能实现自动分析诊断的计算病理学。随着当前计算技术的进步，整个全切片图像可在无人工监督下自主分析。然而，若全切片图像受组织折叠、气泡等伪影影响，根据严重程度不同，分析可能失败或导致误诊。现有伪影检测方法依赖专家进行严重程度评估，以从分析中排除受伪影影响的区域。该过程耗时费力，违背了自动化分析的目标；或直接剔除未评估严重程度的伪影，可能导致重要诊断数据丢失。因此，需先检测伪影再自动评估其严重程度。本文提出一个结合卷积神经网络进行伪影检测与严重程度评估的系统。该系统采用DoubleUNet进行伪影分割，并通过六个微调卷积神经网络模型组成的集成网络确定严重程度。该方法在伪影分割准确率上较现有最优技术提升9%，在严重程度评估上与病理学家评估结果达到97%的高度相关性。通过我们提出的异构数据集验证了系统的鲁棒性，并集成至自动化分析系统中确保了其实用性。