A multi-stage GAN for multi-organ chest X-ray image generation and segmentation

Multi-organ segmentation of X-ray images is of fundamental importance for computer aided diagnosis systems. However, the most advanced semantic segmentation methods rely on deep learning and require a huge amount of labeled images, which are rarely available due to both the high cost of human resources and the time required for labeling. In this paper, we present a novel multi-stage generation algorithm based on Generative Adversarial Networks (GANs) that can produce synthetic images along with their semantic labels and can be used for data augmentation. The main feature of the method is that, unlike other approaches, generation occurs in several stages, which simplifies the procedure and allows it to be used on very small datasets. The method has been evaluated on the segmentation of chest radiographic images, showing promising results. The multistage approach achieves state-of-the-art and, when very few images are used to train the GANs, outperforms the corresponding single-stage approach

A Multi-Stage GAN for Multi-Organ Chest X-ray Image Generation and Segmentation

Multi-organ segmentation of X-ray images is of fundamental importance for computer aided diagnosis systems. However, the most advanced semantic segmentation methods rely on deep learning and require a huge amount of labeled images, which are rarely available due to both the high cost of human resources and the time required for labeling. In this paper, we present a novel multi-stage generation algorithm based on Generative Adversarial Networks (GANs) that can produce synthetic images along with their semantic labels and can be used for data augmentation. The main feature of the method is that, unlike other approaches, generation occurs in several stages, which simplifies the procedure and allows it to be used on very small datasets. The method was evaluated on the segmentation of chest radiographic images, showing promising results. The multi-stage approach achieves state-of-the-art and, when very few images are used to train the GANs, outperforms the corresponding single-stage approach

On inductive-transductive learning with Graph Neural Networks

Many realworld domains involve information naturally represented by graphs, where nodes denote basic patterns while edges stand for relationships among them. The Graph Neural Network (GNN) is a machine learning model capable of directly managing graphstructured data. In the original framework, GNNs are inductively trained, adapting their parameters based on a supervised learning environment. However, GNNs can also take advantage of transductive learning, thanks to the natural way they make information flow and spread across the graph, using relationships among patterns. In this paper, we propose a mixed inductivetransductive GNN model, study its properties and introduce an experimental strategy that allows us to understand and distinguish the role of inductive and transductive learning. The preliminary experimental results show interesting properties for the mixed model, highlighting how the peculiarities of the problems and the data can impact on the two learning strategies

SlAide2Voice: a new educational tool for students with visual disabilities

Online lessons had become more and more frequent and this new way of teaching, forced by the Covid-19 epidemic, implies many problems for all of the students and above all for those ones affected by disabilities. It is therefore absolutely necessary to address this issue and provide practical solutions for it. This paper proposes SlAIde2Voice, a new software pipeline architecture to help visually impaired students to overcome some difficulties related to online lessons. Our aim is to develop a tool, based on three simple components, a client–server architecture, an OpenOffice add-on and an artificial intelligence module, which will be able to help visually impaired students not only during online lessons, but also during their independent study. The proposed methodology promises to improve the learning quality of students with visual difficulties and aims to improve their inclusion and independence. SlAIde2Voice will be designed to be used together with any existing videoconference tools and its use can also be extended to conferences and meetings in general

Multi-stage Synthetic Image Generation for the Semantic Segmentation of Medical Images

Recently, deep learning methods have had a tremendous impact on computer vision applications, from image classification and semantic segmentation to object detection and face recognition. Nevertheless, the training of state-of-the-art neural network models is usually based on the availability of large sets of supervised data. Indeed, deep neural networks have a huge number of parameters which, to be properly trained, require a fairly large dataset of supervised examples. This problem is particularly relevant in the medical field due to privacy issues and the high cost of image tagging by medical experts. In this chapter, we present a new approach that allows to reduce this limitation by generating synthetic images with their corresponding supervision. In particular, this approach can be applied in semantic segmentation, where the generated images (and label-maps) can be used to augment real datasets during network training. The main characteristic of our method, differently from other existing techniques, lies in the generation procedure carried out in multiple steps, based on the intuition that, by splitting the procedure in multiple phases, the overall generation task is simplified. The effectiveness of the proposed multi-stage approach has been evaluated on two different domains, retinal fundus and chest X-ray images. In both domains, the multi-stage approach has been compared with the single-stage generation procedure. The results suggest that generating images in multiple steps is more effective and computationally cheaper, yet allowing high resolution, realistic images to be used for training deep networks

A Two-Stage GAN for High-Resolution Retinal Image Generation and Segmentation

In this paper, we use Generative Adversarial Networks (GANs) to synthesize high-quality retinal images along with the corresponding semantic label-maps, instead of real images during training of a segmentation network. Different from other previous proposals, we employ a two-step approach: first, a progressively growing GAN is trained to generate the semantic label-maps, which describes the blood vessel structure (i.e., the vasculature); second, an image-to-image translation approach is used to obtain realistic retinal images from the generated vasculature. The adoption of a two-stage process simplifies the generation task, so that the network training requires fewer images with consequent lower memory usage. Moreover, learning is effective, and with only a handful of training samples, our approach generates realistic high-resolution images, which can be successfully used to enlarge small available datasets. Comparable results were obtained by employing only synthetic images in place of real data during training. The practical viability of the proposed approach was demonstrated on two well-established benchmark sets for retinal vessel segmentation—both containing a very small number of training samples—obtaining better performance with respect to state-of-the-art techniques

Graph Neural Networks for the Prediction of Protein–Protein Interfaces

Binding site identification allows to determine the functionality and the quaternary structure of protein–protein complexes. Various approaches to this problem have been proposed without reaching a viable solution. Representing the interacting peptides as graphs, a correspondence graph describing their interaction can be built. Finding the maximum clique in the correspondence graph allows to identify the secondary structure elements belonging to the interaction site. Although the maximum clique problem is NP-complete, Graph Neural Networks make for an approximation tool that can solve the problem in affordable time. Our experimental results are promising and suggest that this direction should be explored further

A Two-Stage GAN for High-Resolution Retinal Image Generation and Segmentation

In this paper, we use Generative Adversarial Networks (GANs) to synthesize high-quality retinal images along with the corresponding semantic label-maps, instead of real images during training of a segmentation network. Different from other previous proposals, we employ a two-step approach: first, a progressively growing GAN is trained to generate the semantic label-maps, which describes the blood vessel structure (i.e., the vasculature); second, an image-to-image translation approach is used to obtain realistic retinal images from the generated vasculature. The adoption of a two-stage process simplifies the generation task, so that the network training requires fewer images with consequent lower memory usage. Moreover, learning is effective, and with only a handful of training samples, our approach generates realistic high-resolution images, which can be successfully used to enlarge small available datasets. Comparable results were obtained by employing only synthetic images in place of real data during training. The practical viability of the proposed approach was demonstrated on two well-established benchmark sets for retinal vessel segmentation—both containing a very small number of training samples—obtaining better performance with respect to state-of-the-art techniques

Influence of dietary fat and carbohydrates proportions on plasma lipids, glucose control and low-grade inflammation in patients with type 2 diabetes\u2014The TOSCA.IT Study

The optimal macronutrient composition of the diet for the management of type 2 diabetes is debated, particularly with regard to the ideal proportion of fat and carbohydrates. The aim of the study was to explore the association of different proportions of fat and carbohydrates of the diet-within the ranges recommended by different guidelines-with metabolic risk factors