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.

翻译：摘要：早期诊断和治疗黑色素瘤能够提高患者的生存率。皮肤癌病例预计将增加，而皮肤病理学专家的匮乏凸显了计算病理学（CPATH）系统的必要性。采用深度学习（DL）模型的CPATH系统能够利用潜在的形态学和细胞学特征来识别黑色素瘤的存在。本文提出了一种深度学习方法，用于在全切片图像（WSI）中检测黑色素瘤，并区分正常皮肤与良性/恶性黑色素细胞病变。该方法能以高精度检测病变区域，并在WSI上进行定位，从而为病理学家识别潜在的兴趣区域。值得注意的是，本深度学习方案仅需使用单一卷积神经网络（CNN）首先生成定位图谱，再以其为基础进行切片级预测，从而判断患者是否患有黑色素瘤。最优模型在未见数据上取得了优异的补丁级分类结果，F1分数达0.992，敏感度达0.99。源代码地址：https://github.com/RogerAmundsen/Melanoma-Diagnosis-and-Localization-from-Whole-Slide-Images-using-Convolutional-Neural-Networks。