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：在早期诊断和治疗的乳腺瘤可以提高存活率。预计皮肤癌病例的增加和皮肤病理学家的缺乏突出表明了计算病理系统的必要性。具有深层学习模型的乳腺瘤系统有可能通过利用基本的形态学和细胞特征来辨别乳腺瘤的存在。本文建议了一种DL方法,以检测乳腺瘤,区分正常皮肤和整个幻灯片图像中的良性/良性/显性间皮病。我们的方法非常精确地检测了皮肤病,并将这些病理病理学系统本地化,以查明病理学家感兴趣的潜在区域。有趣的是,我们的DL方法首先依靠使用单一CNN网络创建本地化图,并使用它们进行幻灯片级预测,以确定有乳腺瘤的病人。我们的最佳模型提供了有利的补丁分类结果,其分数为0.992 F1分,对无形数据敏感度为0.999。源代码是 https://github.com/RogerAmund/Melanoma- Diidenalis-Calizis-Neal-Colal-Calization-Cal-Cal-Calizis-Nal-Cal-GLismal-GLis-S-Nal-CLis-NIS-GLis-GLis-MIS-S-M-M-GY-NLis-S-S-S-S-GM-GM-GY)。</s>