组织病理学全切片图像中黑色素瘤皮肤癌的检测与定位
Detection and Localization of Melanoma Skin Cancer in Histopathological Whole Slide Images
AI总结：
针对皮肤癌病例增长、病理医生不足的问题，提出基于单个CNN的深度学习方法，可在全切片图像中检测定位黑色素瘤病变并进行切片级诊断，在未知数据上F1达0.992、灵敏度达0.99。
摘要：
早期诊断并治疗黑色素瘤可提高患者生存率。皮肤癌发病病例预计将增长，同时皮肤病理学家数量不足，这凸显了对计算病理学（CPATH）系统的需求。搭载深度学习（DL）模型的CPATH系统可通过挖掘潜在的形态学与细胞特征，识别黑色素瘤的存在。本文提出一种深度学习方法，用于在全切片图像（WSI，Whole Slide Images）中检测黑色素瘤，并区分正常皮肤与良性/恶性黑素细胞病变。该方法可高精度检测病变，并在WSI上对其进行定位，为病理学家识别潜在的感兴趣区域。值得注意的是，我们的深度学习方法依托单个CNN（卷积神经网络），首先生成本地化图，再利用这些图进行切片级预测，以判定患者是否患有黑色素瘤。我们的最优模型在未见过的数据上取得了优异的斑块级分类结果，F1分数达0.992，灵敏度达0.99。源代码位于：https://github.com/RogerAmundsen/Melanoma-Diagnosis-and-Localization-from-Whole-Slide-Images-using-Convolutional-Neural-Networks。
英文摘要：
Melanoma diagnosed and treated in its early stages can increase the survival rate. A projected increase in skin cancer incidents and a dearth of dermatopathologists have emphasized the need for computational pathology (CPATH) systems. CPATH systems with deep learning (DL) models have the potential to identify the presence of melanoma by exploiting underlying morphological and cellular features. This paper proposes a DL method to detect melanoma and distinguish between normal skin and benign/malignant melanocytic lesions in Whole Slide Images (WSI). Our method detects lesions with high accuracy and localizes them on a WSI to identify potential regions of interest for pathologists. Interestingly, our DL method relies on using a single CNN network to create localization maps first and use them to perform slide-level predictions to determine patients who have melanoma. Our best model provides favorable patch-wise classification results with a 0.992 F1 score and 0.99 sensitivity on unseen data. The source code is https://github.com/RogerAmundsen/Melanoma-Diagnosis-and-Localization-from-Whole-Slide-Images-using-Convolutional-Neural-Networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
