黑素细胞皮肤肿瘤全切片图像中的感兴趣区域检测——痣与黑色素瘤
Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images -- Nevus & Melanoma
AI总结：
本研究针对黑素细胞皮肤肿瘤全切片图像的感兴趣区域检测问题，构建内部深度学习方法实现切片层面的痣与黑色素瘤分类，在测试集上准确率达92.3%，且病理标注区域预测表现优异，可拓展至其他医学图像检测场景。
摘要：
组织病理学图像分析中的自动化感兴趣区域检测是一项具有挑战性且意义重大的课题，对临床实践有着巨大的潜在影响。计算病理学中使用的深度学习方法可帮助我们降低成本，并提高癌症诊断的速度和准确性。我们以UNC Melanocytic Tumor Dataset（北卡罗来纳大学黑素细胞肿瘤数据集）队列为研究基础，该数据集包含160张原发性黑色素瘤（86张）和痣（74张）的苏木精-伊红染色全切片图像。我们随机将80%（134张）的图像划分为训练集，并构建了一种内部深度学习方法，可在切片层面实现痣和黑色素瘤的分类。该方法在其余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92.3%；同时，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，证明了该模型在黑素细胞皮肤肿瘤研究中的出色性能。尽管我们是在皮肤肿瘤数据集上开展的实验，但本研究也可拓展至其他医学图像检测问题，为不同肿瘤的临床评估和诊断提供助力。
英文摘要：
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 that contains 160 hematoxylin and eosin whole-slide images of primary melanomas (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 melanomas. 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 the 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.
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