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：黑色素瘤在早期阶段被诊断和治疗可提高生存率。皮肤癌发病率的预期增长以及皮肤病理学家的短缺，凸显了对计算病理学（CPATH）系统的需求。基于深度学习（DL）模型的CPATH系统具有通过利用潜在的形态和细胞特征识别黑色素瘤存在的潜力。本文提出一种深度学习方法，用于检测黑色素瘤，并区分正常皮肤与全切片图像（WSI）中的良性/恶性黑色素细胞病变。我们的方法能以高准确率检测病变，并在WSI上对其进行定位，从而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赖于仅使用单个卷积神经网络（CNN）首先生成定位图，进而利用这些定位图执行切片级别的预测，以确定患有黑色素瘤的患者。我们的最佳模型在未见数据上获得了优异的斑块级分类结果，F1分数为0.992，灵敏度为0.99。源代码位于：https://github.com/RogerAmundsen/Melanoma-Diagnosis-and-Localization-from-Whole-Slide-Images-using-Convolutional-Neural-Networks。