# MR Images, Brain Lesions, and Deep Learning
## 4 Common Problems in Medical Image Processing for Ischemia and Demyelinating Brain Diseases
### The Dataset  
The availability of large datasets is a major problem in medical imaging studies, and there are few datasets related to specific diseases [27]. The lack of datasets is a challenge since deep learning methods require a large amount of data for training, testing, and validation [33].  
Another major problem is that even though algorithms for ischemic stroke segmentation in MRI scans have been (and are) intensively researched, the reported results in general do not allow us to establish a comparative analysis due to the use of different databases (private and public) with different validation schemes [35, 40].  
The Ischemic Stroke Lesion Segmentation (ISLES) challenge was designed to facilitate the development of tools for the segmentation of stroke lesions [26, 75, 124]. The IschemicStroke Lesion Segmentation (ISLES) group [26, 75] has a set of stroke images, but there is a need to enrich the dataset with clinical information (annotations) in order to get better performance with CNNs.  
Another problem with the datasets is the need for accurately labeled data [43]. This lack of annotated data constitutes a major challenge for ML-supervised algorithms [202] because the methods have to learn and train with limited annotated data, which in most cases contain weak annotations (sparse annotations, noisy annotations, or only image-level annotations) [197]. Therefore, collecting image data in a structured and systematic way is imperative [92] due to the large database required by AI techniques to function efficiently.  
An example of a good practice of health data (images and health information) is exemplified by the UK Biobank [203], which has health data from half a million UK participants. The UK Biobank aims to create a large-scale biomedical database that can be accessed globally for public health research. However, the access depends on administrator approval and payment of a fee.  
Other difficulties that accompany the labeling of the images in a dataset include a lack of collaboration between clinical specialists and academics, patient privacy issues, and, most importantly, the costly, time-consuming task of manual labeling of data by clinicians [40].  
With CNNs, overfitting is a common problem due to the small size of the training data [150], and therefore, it is important the increase of the size of training data. One solution for this problem is the use of the technique of data augmentation, which according to [204] helps improve generalization capabilities of deep neural networks and can be perceived as implicit regularization. For example, [PERSON] et al. [197, 205] reported in their results that the sensitivity of a model improves by 10% (from 62% to 72%) if the dataset is increased from a quarter to full size of the training dataset. Various methods of data augmentation of medical images are reviewed in [206].  
However, in [192], it is suggested that cascaded CNN architectures are a practical solution for the problem of limited annotated data, and the proposed architecture tends to learn well from small sets of data [192].  
An additional but no less important problem is the availability of equipment for collecting image data. Even though MRI is better than CT for stroke diagnosis [18], there is also the fact that in some developing countries, the availability of CT and MRI facilities is very limited and relatively expensive. This is coupled with a lack of suitably trained technical personnel and information [40]. Even in developed countries, there are disparities in the availability of equipment between urban and rural areas. These issues are discussed, for example, in a report published by the Organisation for Economic Co-operation and Development (OECD) [207].