Thyroid cancer is currently the fifth most common malignancy diagnosed in women. Since differentiation of cancer sub-types is important for treatment and current, manual methods are time consuming and subjective, automatic computer-aided differentiation of cancer types is crucial. Manual differentiation of thyroid cancer is based on tissue sections, analysed by pathologists using histological features. Due to the enormous size of gigapixel whole slide images, holistic classification using deep learning methods is not feasible. Patch based multiple instance learning approaches, combined with aggregations such as bag-of-words, is a common approach. This work's contribution is to extend a patch based state-of-the-art method by generating and combining feature vectors of three different patch resolutions and analysing three distinct ways of combining them. The results showed improvements in one of the three multi-scale approaches, while the others led to decreased scores. This provides motivation for analysis and discussion of the individual approaches.

翻译：甲状腺癌目前是女性中诊断出的第五大常见恶性肿瘤。由于癌症亚型的区分对于治疗至关重要，而当前的人工方法耗时且主观，因此实现癌症类型的自动计算机辅助区分至关重要。甲状腺癌的人工区分基于组织切片，由病理学家利用组织学特征进行分析。由于千兆像素全切片图像的尺寸巨大，使用深度学习方法进行整体分类并不可行。基于图像块的多实例学习方法，结合词袋等聚合策略，是一种常见途径。本工作的贡献在于扩展了一种基于图像块的最先进方法，通过生成并融合三种不同分辨率图像块的特征向量，并分析了三种不同的融合方式。结果表明，三种多尺度方法中有一种取得了改进，而其他两种则导致评分下降。这为分析和讨论各具体方法提供了依据。