摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-肿瘤学的新研究-食管癌是一篇报道的主题。根据来自巴西维多利亚的新闻报道，NewsRx记者称，“尽管人工智能和机器学习在医学图像计算方面表现出了显著的性能，但必须提高它们的问责制和透明度，才能将这一成功转化为临床实践。必须解释和解释Mac Hine学习决策的可靠性，特别是为了支持医疗诊断。”我们的新闻编辑引用了信息科学系的一篇研究文章：“为了完成这项任务，必须减轻深度学习技术的黑盒性质，以阐明其有希望的结果，因此，我们旨在探讨ResNet-50深度卷积设计对Barrett食管和腺癌分类的影响，并针对这一任务提出一种两步学习技术。”对构成ResNet-50体系结构的每个卷积层的输出进行了训练和分类，以进一步定义层，从而在体系结构中提供更大的影响。我们表明，局部信息和高维特征对改进分类至关重要。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology-Esophageal Cancer is the subject of a report. According to news reporting originating from Vitoria, Brazil, by NewsRx correspondents, research stated, "Even though artific ial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their accountability and transparency level must be improved to transfer this success into clinical practice. The reliability of mac hine learning decisions must be explained and interpreted, especially for suppor ting the medical diagnosis." Our news editors obtained a quote from the research from the Department of Infor matics, "For this task, the deep learning techniques' black-box nature must some how be lightened up to clarify its promising results. Hence, we aim to investiga te the impact of the ResNet-50 deep convolutional design for Barrett's esophagus and adenocarcinoma classification. For such a task, and aiming at proposing a t wo-step learning technique, the output of each convolutional layer that composes the ResNet-50 architecture was trained and classified for further definition of layers that would provide more impact in the architecture. We showed that local information and high-dimensional features are essential to improve the classifi cation for our task."
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