Intelligent medical image grouping through interactive learning.
Intelligent medical image grouping through interactive learning.
复制标题
通过交互式学习进行智能医学图像分组。
DOI：
10.1007/s41060-016-0021-2
复制
发表时间：
2016
影响因子：
2.4
通讯作者：
Haake,Anne
中科院分区：
文献类型：
--
作者：
Guo,Xuan;Yu,Qi;Li,Rui;Alm,CeciliaOvesdotter;Calvelli,Cara;Shi,Pengcheng;Haake,Anne
知识丰富的领域中的图像分组是具有挑战性的，因为领域知识和人类专业知识是将图像像素转换为有意义的内容的关键。手动标记和注释图像不仅是劳动密集型的，而且效率低下。此外，由于缺乏专家的输入，大多数传统的机器学习方法无法弥补这一差距。因此，我们提出了一个交互式机器学习范式，允许专家成为学习过程中不可分割的一部分。该范例被设计用于自动计算和量化皮肤病学图像的可解释分组。通过这种方式，图像分组模型的计算演化、其可视化和专家交互形成了一个循环来改进图像分组。在我们的范例中，皮肤科医生通过精心设计的界面将一小部分图像分组来编码他们关于医学图像的领域知识。我们的学习算法自动将这些手动指定的连接作为约束，用于重组整个图像数据集。性能评估表明，该范例有效地提高了图像分组的专家知识的基础上。
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.