Automated region of interest detection in histopathological image analysis is a challenging and important topic with tremendous potential impact on clinical practice. The deep learning methods used in computational pathology may help us to reduce costs and increase the speed and accuracy of cancer diagnosis. We started with the UNC Melanocytic Tumor Dataset cohort which contains 160 hematoxylin and eosin whole slide images of primary melanoma (86) and nevi (74). We randomly assigned 80% (134) as a training set and built an in-house deep learning method to allow for classification, at the slide level, of nevi and melanoma. The proposed method performed well on the other 20% (26) test dataset; the accuracy of the slide classification task was 92.3% and our model also performed well in terms of predicting the region of interest annotated by the pathologists, showing excellent performance of our model on melanocytic skin tumors. Even though we tested the experiments on a skin tumor dataset, our work could also be extended to other medical image detection problems to benefit the clinical evaluation and diagnosis of different tumors.
组织病理学图像分析中的自动感兴趣区域检测是一个具有挑战性且重要的课题，对临床实践具有巨大的潜在影响。计算病理学中使用的深度学习方法可能有助于我们降低成本，并提高癌症诊断的速度和准确性。我们以UNC黑色素细胞肿瘤数据集队列为起点，该队列包含160张原发性黑色素瘤（86例）和痣（74例）的苏木精和伊红全切片图像。我们随机分配80%（134张）作为训练集，并构建了一种内部深度学习方法，用于在切片水平上对痣和黑色素瘤进行分类。所提出的方法在另外20%（26张）的测试数据集上表现良好；切片分类任务的准确率达到92.3%，我们的模型在预测病理学家标注的感兴趣区域方面也表现优异，显示出我们的模型在黑色素细胞皮肤肿瘤上的卓越性能。尽管我们在皮肤肿瘤数据集上进行了实验测试，但我们的工作也可以扩展到其他医学图像检测问题，以促进不同肿瘤的临床评估和诊断。
Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images—Nevus and Melanoma
肿瘤(癌症)患者之家