1,721,114 research outputs found
Computational intelligence in gamesProceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion - GECCO Companion '12
No full textAre 3D better than 2D Convolutional Neural Networks for Medical Imaging Semantic Segmentation?
No full textIn the last decade, Deep Learning has revolutionized Computer Vision thanks to Convolutional Neural Networks (CNN), that achieved state-of-the-art results in many tasks. In the medical field, imaging techniques, like MRI and CT, are widely used to acquire 3D images of regions that need to be analyzed to identify targets or regions of interest (ROIs). In particular, semantic segmentation is a common image processing task involved in several clinical procedures. When using Deep Learning to solve this task it is possible to either apply a 2D CNN to each slice of the acquired 3D image or apply a 3D CNN to the entire volume acquired. Despite both this approaches have been investigated in the literature, there is neither yet a clear understanding of which one is better (if this is the case) nor a fair comparison of their performances on the same datasets. In this work we aim at making a first step toward to providing an empirical guidance on choosing between 2D and 3D CNNs for medical imaging segmentation. To this purpose we compared a 2D CNN and a 3D CNN based on deep residual U-Net (ResUnet) architecture on different datasets. Our results suggest that the potential benefits of using a 3D CNN are difficult to exploit due to the very limited amount of data that is typically available in medical datasets
A Tool for the Procedural Generation of Shaders Using Interactive Evolutionary Algorithms
No full textWe present a tool for exploring the design space of shaders using an interactive evolutionary algorithm integrated with the Unity editor, a well-known commercial tool for video game development. Our framework leverages the underlying graph-based representation of recent shader editors and inter-active evolution to let designers explore several visual options starting from an existing shader. Our framework encodes the graph representation of a current shader as a chromosome used to seed the evolution of a shader population. It applies graph- based recombination and mutation with a set of heuristics to create feasible shaders. The framework is an extension of the Unity editor; thus, designers with little knowledge of evolutionary computation (and shader programming) can interact with the underlying evolutionary engine using the same visual interface used for working on game scenes
Brain MRI Tumor Segmentation with Adversarial Networks
Full text linkDeep Learning is a promising approach to either automate or simplify several tasks in the healthcare domain. In this work, we introduce SegAN-CAT, an end-to-end approach to brain tumor segmentation in Magnetic Resonance Images (MRI), based on Adversarial Networks. In particular, we extend SegAN, successfully applied to the same task in a previous work, in two respects: (i) we used a different model input and (ii) we employed a modified loss function to train the model. We tested our approach on two large datasets, made available by the Brain Tumor Image Segmentation Benchmark (BraTS). First, we trained and tested some segmentation models assuming the availability of all the major MRI contrast modalities, i.e., T1-weighted, T1 weighted contrast enhanced, T2-weighted, and T2-FLAIR. However, as these four modalities are not always all available for each patient, we also trained and tested four segmentation models that take as input MRIs acquired with a single contrast modality. Finally, we proposed to apply transfer learning across different contrast modalities to improve the performance of these single-modality models. Our results are promising and show that not only SegAN-CAT is able to outperform SegAN when all the four modalities are available, but also that transfer learning can actually lead to better performances when only a single modality is available
- …
