Towards novel methods for effective transfer learning and unsupervised deep learning for medical image analysis
Thanks to computational and algorithmic advances, as well as an increasing availability of vast amounts of data, deep learning techniques have substantially improved over the past decade (LeCun et al. 2015) Specifically, in recent years, deep learning techniques have been successfully applied to the field of image analysis (Szegedy et al. 2016a) speech recognition (Hinton et al. 2012) and natural language processing (Mikolov et al. 2013) showing that they are increasingly able to outperform traditional machine learning approaches that typically rely on manual feature engineering. Furthermore, in collaboration with healthcare institutes, companies like Google and IBM have recently started with the application of deep learning techniques to medical use cases. As an example, the authors of (Wong and Bressler, 2016) discuss the usage of deep learning techniques for diagnosing diabetic retinopathy, an eye disease that occurs when diabetes causes damage to the retina. Compared to the application of conventional machine learning approaches to medical images, the application of deep learning techniques remains challenging. Indeed, medical image sets are often limited in size and (partially) unlabeled (Chen and Lin, 2014) due to privacy concerns, acquisition difficulties, and/or the time consuming nature of manual labeling. However, when applying deep learning techniques, the following rule of thumb usually holds true: the more data that can be leveraged during training, the higher the effectiveness of prediction (LeCun et al. 2015) As a result, given that it is difficult to get access to vast collections of properly labeled medical images, predictive models obtained through the usage of deep learning techniques typically suffer from overfitting, thus leading to inaccurate diagnoses. Restrictions in terms of size and labeling are not limited to medical datasets; datasets in other application areas are facing these challenges as well (Santoro et al. 2016) Therefore, more and more research efforts are dedicated to addressing these shortcomings. One promising approach towards dealing with small sized datasets is the usage of transfer learning, a technique that can be used to improve a model from one domain by leveraging knowledge from a related domain. Compared to training from scratch with small datasets, experimental analysis has demonstrated that transfer learning may reduce the relative error with up to 50% (Yosinski et al. 2014; Azizpour et al. 2015) However, compared to training from scratch with vast datasets, there is still significant room for improvement (Szegedy et al. 2016a) Another interesting approach towards dealing with small sized datasets, as well as with a lack of labeled samples, is the usage of unsupervised deep learning, which allows exposing structure and semantics in unlabeled datasets. Indeed, several unsupervised deep learning techniques have recently been introduced, for instance making it possible to generate similar images out of a given set of images. Our doctoral research will focus on the construction and evaluation of new predictive models for medical image diagnosis, through the development of novel methods for effective transfer learning and unsupervised deep learning, so to be able to overcome limitations in terms of size and labeling. In the following section, we outline a number of relevant research questions that we set out to answer.