Robust classification techniques for medical imaging

机器学习技术对医学图像分析领域产生了巨大的影响，并已成功应用于各种分类，分割和配准问题。然而，在采用在一个特定研究数据集上训练的分类算法并将其转化为常规临床实践方面存在重大挑战，其中在不同患者人群的不同中心获得的图像可能具有非常不同的特征。由于收集具有专家注释或地面实况诊断的高质量训练图像的高成本，该问题变得更加困难。 医学成像界通常通过使用关于图像的物理特性的知识来规范化输入数据来解决这些问题，例如通过在提取用于分类的特征之前使用强度重新缩放或空间插值。最近，计算机科学中已经开发了域自适应或迁移学习技术，其目的是调整在一个数据集上学习的分类器，以科普目标数据集中的差异。这些方法通常涉及利用从标记的训练数据中学习到的信息来对未见过的目标数据进行分类，其中很少或没有可用的注释;示例包括基于训练案例与测试案例的相似性对训练案例进行重新加权，导出将特征从一个域映射到另一个域的变换函数以及半监督聚类技术。迄今为止，在医学成像领域，关于半监督学习和领域自适应的研究很少。在临床实践中广泛采用机器学习技术存在一些重大障碍;标记数据集通常比机器视觉中使用的数据集小几个数量级，但同时对高度准确的结果的需求要大得多。分类器还必须处理图像质量和采集参数的广泛变化。 我们建议探索现有的半监督学习和领域自适应方法，并开发新的方法来科普医学成像中遇到的具体问题;我们将专注于两个非常不同的临床应用，即乳腺MRI和数字病理学，但我们开发的方法将推广到其他应用，并将有助于加快机器学习算法在研究实验室外的翻译。并转化为常规用于解决临床问题的软件包。

Machine learning techniques have had an enormous impact on the field of medical image analysis and have been successfully applied to a wide variety of classification, segmentation and registration problems. There are, however, significant challenges in taking a classification algorithm trained on one specific research dataset and translating it to routine clinical practice where images acquired at different centres in different patient populations may have very different characteristics. The problem is made even more difficult by the high cost of collecting good quality training images complete with expert annotations or ground truth diagnoses. The medical imaging community have typically addressed these issues by using knowledge about the physical characteristics of the images to normalize the input data, for example by using intensity rescaling or spatial interpolation before extracting features for classification. More recently domain adaptation or transfer learning techniques have been developed in computer science which aim to adjust classifiers learnt on one data set to cope with differences in the target dataset. These methods generally involve leveraging the information learnt from the labelled training data to classify the unseen target data where there are either few or no annotations available; examples include re-weighting of training cases based on their similarity to the test cases, deriving transformation functions that map features from one domain into another and semi-supervised clustering techniques. There has been very little research on semi-supervised learning and on domain adaptation in the medical imaging field to date. There are some significant obstacles to the widespread adoption of machine learning techniques in clinical practice; labelled data sets are typically orders of magnitude smaller than those used in machine vision but at the same time there is a much greater need for highly accurate results. Classifiers also have to deal with a wide variation in image quality and acquisition parameters. We propose to explore existing methods of semi-supervised learning and domain adaptation and also develop novel approaches to cope with the specific problems encountered in medical imaging; we will concentrate on two very different clinical applications namely breast MRI and digital pathology however the approaches we develop will be generalizable to other applications and will help to speed the translation of machine learning algorithms out of the research lab and into software packages used routinely to solve clinical problems.