Whole Image Synthesis using a Deep Encoder-Decoder Network

The synthesis of medical images is an intensity transformation of a given modality in a way that represents an acquisition with a different modality (in the context of MRI this represents the synthesis of images originating from different MR sequences). Most methods follow a patch-based approach, which is computationally inefficient during synthesis and requires some sort of ‘fusion’ to synthesize a whole image from patch-level results. In this paper, we present a whole image synthesis approach that relies on deep neural networks. Our architecture resembles those of encoder-decoder networks, which aims to synthesize a source MRI modality to an other target MRI modality. The proposed method is computationally fast, it doesn’t require extensive amounts of memory, and produces comparable results to recent patch-based approaches

Defect detection using weakly supervised learning

In many real-world scenarios, obtaining large amounts of labeled data can be a daunting task. Weakly supervised learning techniques have gained significant attention in recent years as an alternative to traditional supervised learning, as they enable training models using only a limited amount of labeled data. In this paper, the performance of a weakly supervised classifier to its fully supervised counterpart is compared on the task of defect detection. Experiments are conducted on a dataset of images containing defects, and evaluate the two classifiers based on their accuracy, precision, and recall. Our results show that the weakly supervised classifier achieves comparable performance to the supervised classifier, while requiring significantly less labeled data

A machine learning approach to analysis and classification of measurements in cultural heritage

Treatment of spectral information is an essential tool for the examination of various cultural heritage materials. Raman Spectroscopy has become an everyday practice for compound identification due to its non-intrusive nature, but often it can be a complex operation. Spectral identification and analysis on artists' materials is being done with the aid of already existing spectral databases and spectrum matching algorithms. We demonstrate that with a machine learning method called Extremely Randomised Trees, we can learn a model in a supervised learning fashion, able to accurately match an entire-spectrum range into its respective mineral. Our approach was tested and was found to outperform the state-of-the-art methods on the corrected RRUFF dataset, while maintaining low computational complexity and inherently supporting parallelisation

Tackling Dataset Bias With an Automated Collection of Real-World Samples

The early 21st-century technological advancements tilted the scales towards data-driven learning. Thus, modern machine-learning systems rely heavily on data to learn complex models to efficiently provide relevant predictions. Data-driven learning suffers from overfitting, a situation in which the learning process seems to have converged into a model that, unfortunately, lacks generalization power. One way to withstand overfitting is to expand the training dataset with more diverse samples. Typically, this is implemented (particularly in computer vision research, which is of interest in this study) by multiplying the original sample using several transformations. Although this strategy might seem straightforward, it does not affect any preexisting dataset bias because the initial distribution remains more or less similar. Ideally, new samples of unseen data must be found, but the cost of acquiring them individually is high. This study presents a novel pipeline that combines state-of-the-art modules to automatically create new thematic datasets with low bias. The proposed method was able to acquire and allocate more than 880K previously unseen images to produce a data collection, that InceptionV3 classified it with 72% accuracy and achieved 0.0008 performance variance when testing on similar datasets

Deep Learning-Based Black Spot Identification on Greek Road Networks

Black spot identification, a spatiotemporal phenomenon, involves analysing the geographical location and time-based occurrence of road accidents. Typically, this analysis examines specific locations on road networks during set time periods to pinpoint areas with a higher concentration of accidents, known as black spots. By evaluating these problem areas, researchers can uncover the underlying causes and reasons for increased collision rates, such as road design, traffic volume, driver behaviour, weather, and infrastructure. However, challenges in identifying black spots include limited data availability, data quality, and assessing contributing factors. Additionally, evolving road design, infrastructure, and vehicle safety technology can affect black spot analysis and determination. This study focused on traffic accidents in Greek road networks to recognize black spots, utilizing data from police and government-issued car crash reports. The study produced a publicly available dataset called Black Spots of North Greece (BSNG) and a highly accurate identification method

Sustainable Ecotourism through Cutting-Edge Technologies

Tourism is a phenomenon that dates back to ancient times. Ancient Greek philosophers recognised, adopted, and promoted the concept of rest-based tourism. Ecotourism is a particular type of tourism that connects with activities that take place in nature, without harming it, along with the herbal and animal wealth. According to estimates, the global ecotourism industry is currently booming due to various reasons, and it is becoming an important factor of sustainable regional development. This article presents the vision, work, and outcomes of project AdVENt, a project focusing natively in sustainable ecotourism through natural science and technological innovation. AdVENt’s study area includes the National Parks of Oiti (or Oeta) and Parnassus in Central Greece, where there is a remarkable native flora with a high endemism rate integrated with areas of cultural value and national and European hiking routes and paths of varying difficulty

Sustainable Ecotourism through Cutting-Edge Technologies

Tourism is a phenomenon that dates back to ancient times. Ancient Greek philosophers recognised, adopted, and promoted the concept of rest-based tourism. Ecotourism is a particular type of tourism that connects with activities that take place in nature, without harming it, along with the herbal and animal wealth. According to estimates, the global ecotourism industry is currently booming due to various reasons, and it is becoming an important factor of sustainable regional development. This article presents the vision, work, and outcomes of project AdVENt, a project focusing natively in sustainable ecotourism through natural science and technological innovation. AdVENt’s study area includes the National Parks of Oiti (or Oeta) and Parnassus in Central Greece, where there is a remarkable native flora with a high endemism rate integrated with areas of cultural value and national and European hiking routes and paths of varying difficulty