Scale-Aware Transformers for Diagnosing Melanocytic Lesions.
Scale-Aware Transformers for Diagnosing Melanocytic Lesions.
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DOI：
10.1109/access.2021.3132958
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Shapiro LG
中科院分区：
文献类型：
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作者：
Wu W;Mehta S;Nofallah S;Knezevich S;May CJ;Chang OH;Elmore JG;Shapiro LG
诊断黑素细胞病变是最具挑战性的病理学领域之一，具有广泛的观察者内和观察者间的差异。诊断侵袭性黑色素瘤的金标准是由经验丰富的皮肤病理学家检查组织病理学全载玻片皮肤活检图像。数字化的全切片图像为计算机程序提供了新的机会，以提高病理学家的诊断性能。为了自动分类这些图像，需要反映输入图像的内容和上下文的表示。在本文中，我们介绍了一种新的自我注意力为基础的网络学习表示从数字全载玻片图像的黑色素细胞皮肤病变在多个尺度。我们的模型从多个尺度上柔和地权衡表示，使其能够自动区分诊断相关和不相关的信息。我们的实验表明，我们的方法优于其他五个国家的最先进的整体幻灯片图像分类方法的显着保证金。我们的方法也实现了与187名执业美国病理学家在独立研究中解释相同病例的性能相当。为了促进相关研究，完整的训练和推理代码在https://github.com/meredith-wenjunwu/ScATNet上公开。
Diagnosing melanocytic lesions is one of the most challenging areas of pathology with extensive intra- and inter-observer variability. The gold standard for a diagnosis of invasive melanoma is the examination of histopathological whole slide skin biopsy images by an experienced dermatopathologist. Digitized whole slide images offer novel opportunities for computer programs to improve the diagnostic performance of pathologists. In order to automatically classify such images, representations that reflect the content and context of the input images are needed. In this paper, we introduce a novel self-attention-based network to learn representations from digital whole slide images of melanocytic skin lesions at multiple scales. Our model softly weighs representations from multiple scales, allowing it to discriminate between diagnosis-relevant and -irrelevant information automatically. Our experiments show that our method outperforms five other state-of-the-art whole slide image classification methods by a significant margin. Our method also achieves comparable performance to 187 practicing U.S. pathologists who interpreted the same cases in an independent study. To facilitate relevant research, full training and inference code is made publicly available at https://github.com/meredith-wenjunwu/ScATNet.