Guo Xuan, Yu Qi, Li Rui, Alm Cecilia Ovesdotter, Calvelli Cara, Shi Pengcheng, Haake Anne
B. Thomas Golisano College of Computing and Information Sciences, Rochester Institute of Technology, 20 Lomb Memorial Drive, Rochester, NY 14623, USA.
College of Liberal Arts, Rochester Institute of Technology, 92 Lomb Memorial Drive, 14623 Rochester, NY, USA.
Int J Data Sci Anal. 2016 Dec;2(3-4):95-105. doi: 10.1007/s41060-016-0021-2. Epub 2016 Aug 25.
Image grouping in knowledge-rich domains is challenging, since domain knowledge and human expertise are key to transform image pixels into meaningful content. Manually marking and annotating images is not only labor-intensive but also ineffective. Furthermore, most traditional machine learning approaches cannot bridge this gap for the absence of experts' input. We thus present an interactive machine learning paradigm that allows experts to become an integral part of the learning process. This paradigm is designed for automatically computing and quantifying interpretable grouping of dermatological images. In this way, the computational evolution of an image grouping model, its visualization, and expert interactions form a loop to improve image grouping. In our paradigm, dermatologists encode their domain knowledge about the medical images by grouping a small subset of images via a carefully designed interface. Our learning algorithm automatically incorporates these manually specified connections as constraints for reorganizing the whole image dataset. Performance evaluation shows that this paradigm effectively improves image grouping based on expert knowledge.
在知识丰富的领域中进行图像分组具有挑战性，因为领域知识和人类专业知识是将图像像素转化为有意义内容的关键。手动标记和注释图像不仅劳动强度大，而且效率低下。此外，由于缺乏专家输入，大多数传统机器学习方法无法弥合这一差距。因此，我们提出了一种交互式机器学习范式，使专家能够成为学习过程中不可或缺的一部分。这种范式旨在自动计算和量化皮肤病图像的可解释分组。通过这种方式，图像分组模型的计算演化、其可视化以及专家交互形成一个循环，以改进图像分组。在我们的范式中，皮肤科医生通过精心设计的界面将一小部分图像分组，从而对他们关于医学图像的领域知识进行编码。我们的学习算法会自动将这些手动指定的连接作为约束条件，用于重新组织整个图像数据集。性能评估表明，这种范式基于专家知识有效地改进了图像分组。
