Degenerative adversarial neuroimage nets for brain scan simulations: Application in ageing and dementia

Accurate and realistic simulation of high-dimensional medical images has become an important research area relevant to many AI-enabled healthcare applications. However, current state-of-the-art approaches lack the ability to produce satisfactory high-resolution and accurate subject-specific images. In this work, we present a deep learning framework, namely 4D-Degenerative Adversarial NeuroImage Net (4D-DANI-Net), to generate high-resolution, longitudinal MRI scans that mimic subject-specific neurodegeneration in ageing and dementia. 4D-DANI-Net is a modular framework based on adversarial training and a set of novel spatiotemporal, biologically-informed constraints. To ensure efficient training and overcome memory limitations affecting such high-dimensional problems, we rely on three key technological advances: i) a new 3D training consistency mechanism called Profile Weight Functions (PWFs), ii) a 3D super-resolution module and iii) a transfer learning strategy to fine-tune the system for a given individual. To evaluate our approach, we trained the framework on 9852 T1-weighted MRI scans from 876 participants in the Alzheimer's Disease Neuroimaging Initiative dataset and held out a separate test set of 1283 MRI scans from 170 participants for quantitative and qualitative assessment of the personalised time series of synthetic images. We performed three evaluations: i) image quality assessment; ii) quantifying the accuracy of regional brain volumes over and above benchmark models; and iii) quantifying visual perception of the synthetic images by medical experts. Overall, both quantitative and qualitative results show that 4D-DANI-Net produces realistic, low-artefact, personalised time series of synthetic T1 MRI that outperforms benchmark models

Visual Feature Attribution using Wasserstein GANs

Attributing the pixels of an input image to a certain category is an important and well-studied problem in computer vision, with applications ranging from weakly supervised localisation to understanding hidden effects in the data. In recent years, approaches based on interpreting a previously trained neural network classifier have become the de facto state-of-the-art and are commonly used on medical as well as natural image datasets. In this paper, we discuss a limitation of these approaches which may lead to only a subset of the category specific features being detected. To address this problem we develop a novel feature attribution technique based on Wasserstein Generative Adversarial Networks (WGAN), which does not suffer from this limitation. We show that our proposed method performs substantially better than the state-of-the-art for visual attribution on a synthetic dataset and on real 3D neuroimaging data from patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). For AD patients the method produces compellingly realistic disease effect maps which are very close to the observed effects.Comment: Accepted to CVPR 201

Evaluating the effect of curing conditions on the glass transition of the structural adhesive using conditional tabular generative adversarial networks

Owing to its structural advantages, adhesively bonding fibre-reinforced polymers have been a promising solution for strengthening constructions. However, the effectiveness of this technique is significantly influenced by the material properties of the adhesive layer, which are largely determined by its curing condition. A comprehensive analysis of the effect of curing conditions on structural adhesives is hampered by the lack of sufficient experimental data. To mitigate such a limitation, this present study utilises a deep machine learning (ML) tool, the conditional tabular generative adversarial networks (CTGAN), to generate plausible synthetic dataset for developing a robust data-driven model. An artificial neural network (ANN) was trained on synthetic data and tested on real data, following the "Train on Synthetic – Test on Real" philosophy. The ultimately developed CTGAN-ANN model was validated by newly conducted experiments and several published studies (R2 ≥ 0.95), which demonstrated the ability to provide accurate estimates of the glass transition temperature values of the polymer adhesive. A comprehensive evaluation of the effect of each curing condition variable on the adhesive was performed, which revealed the underlying relationships, indicating that curing temperature and curing time have a positive effect, but that curing humidity has a negative effect. The ML model developed could inform the practical use of the structural adhesive in civil engineering

Revolutionising inverse design of magnesium alloys through generative adversarial networks

The utility of machine learning (ML) techniques in materials science has accelerated materials design and discovery. However, the accuracy of ML models - particularly deep neural networks - heavily relies on the quality and quantity of the training data. Data collection methods often have limitations arising from cost, difficulty, and resource-intensive human efforts. Thus, limited high-quality data, especially for novel materials, poses a significant challenge in developing reliable ML models. Generative adversarial networks (GANs) offer one solution to augment datasets through synthetic sample generation. The present work explores the application of GANs in magnesium (Mg) alloy design, by training two deep neural networks within the structure of a Wasserstein GAN to generate new (novel) alloys with desired mechanical properties. This data augmentation-based strategy contributes to model robustness, particularly in cases where traditional data collection is impractical. The approach presented may expedite Mg alloy development, through a GAN assisted inverse design approach.Comment: 23 pages, 4 figures, 2 tables, 1 Github repositor

A data augmentation strategy for improving age estimation to support CSEM detection

[EN] Leveraging image-based age estimation in preventing Child Sexual Exploitation Material (CSEM) content over the internet is not investigated thoroughly in the research community. While deep learning methods are considered state-of-the-art for general age estimation, they perform poorly in predicting the age group of minors and older adults due to the few examples of these age groups in the existing datasets. In this work, we present a data augmentation strategy to improve the performance of age estimators trained on imbalanced data based on synthetic image generation and artificial facial occlusion. Facial occlusion is focused on modelling as CSEM criminals tend to cover certain parts of the victim, such as the eyes, to hide their identity. The proposed strategy is evaluated using the Soft Stagewise Regression Network (SSR-Net), a compact size age estimator and three publicly available datasets composed mainly of non-occluded images. Therefore, we create the Synthetic Augmented with Occluded Faces (SAOF-15K) dataset to assess the performance of eye and mouthoccluded images. Results show that our strategy improves the performance of the evaluated age estimator

Reconstruction of Iberian ceramic potteries using generative adversarial networks

Several aspects of past culture, including historical trends, are inferred from time-based patterns observed in archaeological artifacts belonging to different periods. The presence and variation of these objects provides important clues about the Neolithic revolution and given their relative abundance in most archaeological sites, ceramic potteries are significantly helpful in this purpose. Nonetheless, most available pottery is fragmented, leading to missing morphological information. Currently, the reassembly of fragmented objects from a collection of thousands of mixed fragments is a daunting and time-consuming task done almost exclusively by hand, which requires the physical manipulation of the fragments. To overcome the challenges of manual reconstruction and improve the quality of reconstructed samples, we present IberianGAN, a customized Generative Adversarial Network (GAN) tested on an extensive database with complete and fragmented references. We trained the model with 1072 samples corresponding to Iberian wheel-made pottery profiles belonging to archaeological sites located in the upper valley of the Guadalquivir River (Spain). Furthermore, we provide quantitative and qualitative assessments to measure the quality of the reconstructed samples, along with domain expert evaluation with archaeologists. The resulting framework is a possible way to facilitate pottery reconstruction from partial fragments of an original piece.Fil: Navarro, Jose Pablo. Universidad Nacional de la Patagonia "San Juan Bosco". Facultad de Ingeniería - Sede Puerto Madryn. Departamento de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Ciencias Sociales y Humanas; ArgentinaFil: Cintas, Celia. Catholic University Of Eastern Africa; KeniaFil: Lucena, Manuel. Universidad de Jaén; EspañaFil: Fuertes, José Manuel. Universidad de Jaén; EspañaFil: Segura, Rafael. Universidad de Jaén; EspañaFil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur; ArgentinaFil: Gonzalez-Jose, Rolando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Ciencias Sociales y Humanas; Argentin