Simplifying the usage and construction of deep image classification models stars

Resum

Artificial Intelligence, and specifically Deep Learning, has gained great importance in recent years due to the rapid increase in processing capacity, the availability of a large amount of data and the emergence of different open source libraries that allow its use in a simple and free way. Due to this fact, Deep Learning techniques have become the state-of-the-art approach to work on different scientific problems and specifically on image analysis. Image analysis problems often require repetitive time-consuming tasks, and Deep Learning techniques are able to solve these repetitive tasks faster, and in an efficient way. Specifically, these techniques have allowed great advances in different fields such as security, medicine or biology. However, the use of Deep Learning techniques is not trivial since they require a large amount of computational resources, for example specific hardware such as GPUS or TPUS. In addition, it is necessary to have a large amount of annotated data, a requirement that in fields such as medicine or biology can be difficult to fulfil. Finally, expert knowledge of these techniques is required both to build Deep Learning models and to use them. These needs hinder the adoption and democratisation of Deep Learning methods in fields such as medicine or biology where the amount of data resources is limited, and in general, outside the field of computer science due to the need to expert knowledge. Thus, we have identified three challenges related to the use of Deep Learning techniques: the amount of data necessary for the use of these techniques, the democratisation of the construction of Deep Learning models, and the democratisation of the use of Deep Learning models. The objective of this work is to analyse these challenges and create techniques and tools that help mitigate them in the context of image classification models. First of all, we have focused on reducing the amount of data required to use Deep Learning techniques. In particular, we have developed a framework called CLoDSA that allows anyone to use data augmentation methods for image classification, detection and segmentation problems. In addition, we have created two semi-supervised learning algorithms that allow us to train Deep Learning models using annotated and non-annotated data. The first algorithm is based on data and model distillation, whereas the second uses topological data analysis techniques. In order to democratise the construction of Deep image classifications models, we have developed an AutoML tool, called ATLASS, which assists the user in the entire process of creating an image classification model, from annotating the images, yo the creation and usage of such a Deep Learning model. This tool has been validated with several datasets obtaining better results than other AutoML tools. The problem of democratising the use of Deep Learning models has been approached in two different ways. In the first place, to reduce the amount of resources needed to use Deep Learning models, we have studied the combination of a semi-supervised method with compact networks and quantification techniques. This approach has reduced the amount of computational resources needed to train and use Deep Learning models. Moreover, models created with this approach have similar or even better performance than standard size models and are also faster and lighter. Secondly, the democratisation of the use of Deep Learning models has been addressed by creating a framework called DeepClas4Bio, which provides a common access point for the classification models of various Deep Learning libraries and facilitates the interoperability of bioimaging tools with Deep Learning models. In addition, a series of plugins have been created to connect the main biomedical tools with such a framework. Finally, the aforementioned techniques have been the basis to deal with two real biomedical problems: the measurement of the propagation of bacteria in motility images, and the detection of diseases of the epiretinal membrane from fundus images.