走向自动化黑色素瘤筛查：正确的计算机视觉和可靠的结果
Towards Automated Melanoma Screening: Proper Computer Vision & Reliable Results
摘要：
在本文中，我们调查，分析和批评目前的艺术自动黑色素瘤筛查，重新实施的基线技术，并提出了两个新的。黑色素瘤虽然在早期发现时高度可治愈，但由于诊断和治疗的延迟，最终成为最危险的癌症类型之一。它的发病率正在飙升，远远超过了能够诊断出它的训练有素的专业人员的数量。自动筛查似乎是一种替代方案，可以充分利用这些专业人员，将他们的时间集中在有风险的病人身上，同时安全地让其他病人出院。然而，由于当前文献强调过时的计算机视觉模型，自动化黑素瘤诊断的潜力目前尚未实现。我们展示了基于当前计算机视觉的流水线如何优于传统模型-基于高级词袋的模型达到84.6%的AUC，基于深度神经网络的模型达到89.3%，而基线（经典词袋）保持在81.2%。我们还发起对话，以提高我们社区的可重复性
英文摘要：
In this paper we survey, analyze and criticize current art on automated melanoma screening, reimplementing a baseline technique, and proposing two novel ones. Melanoma, although highly curable when detected early, ends as one of the most dangerous types of cancer, due to delayed diagnosis and treatment. Its incidence is soaring, much faster than the number of trained professionals able to diagnose it. Automated screening appears as an alternative to make the most of those professionals, focusing their time on the patients at risk while safely discharging the other patients. However, the potential of automated melanoma diagnosis is currently unfulfilled, due to the emphasis of current literature on outdated computer vision models. Even more problematic is the irreproducibility of current art. We show how streamlined pipelines based upon current Computer Vision outperform conventional models - a model based on an advanced bags of words reaches an AUC of 84.6%, and a model based on deep neural networks reaches 89.3%, while the baseline (a classical bag of words) stays at 81.2%. We also initiate a dialog to improve reproducibility in our community
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。
