Analysis and Classification of Event-Related Potentials During Image Observation.

In the field of cognitive neuroscience, researchers have conducted extensive studies on object categorization using Event-Related Potential (ERP) analysis, specifically by analyzing electroencephalographic (EEG) response signals triggered by visual stimuli. The most common approach for visual ERP analysis is to use a low presentation rate of images and an active task where participants actively discriminate between target and non-target images. However, researchers are also interested in understanding how the human brain processes visual information in real-world scenarios.

Towards an Informed CNN for Bone SR-microCT Image Classification with an Unsupervised Patched-based Image Clustering.

Bone microscale differences cannot be readily recognizable to humans from Synchrotron Radiation micro-Computed Tomography (SR-microCT) images. Premises are possible with Deep Learning (DL) imaging analysis. Despite this, more attention to high-level features leads models to require help identifying relevant details to support a decision.

Rethinking Theta/Beta Ratio in ADHD through Functional Data Analysis.

ADHD is a neurodevelopmental disorder largely diffused among children and adolescents. The current method of diagnosis is based on agreed clinical literature such as DSM-5, by identifying and evaluating signs of hyperactivity and inattention. Multiple reviews have assessed that EEG is not sufficiently reliable for the diagnosis of ADHD.

Anatomically compliant modes of variations: New tools for brain connectivity.

Anatomical complexity and data dimensionality present major issues when analysing brain connectivity data. The functional and anatomical aspects of the connections taking place in the brain are in fact equally relevant and strongly intertwined. However, due to theoretical challenges and computational issues, their relationship is often overlooked in neuroscience and clinical research.

NERONE: The Fast Way to Efficiently Execute Your Deep Learning Algorithm at the Edge.

Semantic segmentation and classification are pivotal in many clinical applications, such as radiation dose quantification and surgery planning. While manually labeling images is highly time-consuming, the advent of Deep Learning (DL) has introduced a valuable alternative. Nowadays, DL models inference is run on Graphics Processing Units (GPUs), which are power-hungry devices, and, therefore, are not the most suited solution in constrained environments where Field Programmable Gate Arrays (FPGAs) become an appealing alternative given their remarkable performance per watt ratio.

Math Skills: a New Look from Functional Data Analysis.

Mental calculations involve various areas of the brain. The frontal, parietal and temporal lobes of the left hemisphere have a principal role in the completion of this typology of tasks. Their level of activation varies based on the mathematical competence and attentiveness of the subject under examination and the perceived difficulty of the task.

An Optimized U-Net for Unbalanced Multi-Organ Segmentation.

Medical practice is shifting towards the automation and standardization of the most repetitive procedures to speed up the time-to-diagnosis. Semantic segmentation repre-sents a critical stage in identifying a broad spectrum of regions of interest within medical images. Indeed, it identifies relevant objects by attributing to each image pixels a value representing pre-determined classes.

On the Automation of Radiomics-Based Identification and Characterization of NSCLC.

Proper detection and accurate characterization of Non-Small Cell Lung Cancer (NSCLC) are an open challenge in the imaging field. Biomedical imaging is fundamental in lung cancer assessment and offers the possibility of calculating predictive biomarkers impacting patients' management. Within this context, radiomics, which consists of extracting quantitative features from digital images, shows encouraging results for clinical applications, but the sub-optimal standardization of the procedure and the lack of definitive results are still a concern in the field.

A Functional Data Analysis Approach to Left Ventricular Remodeling Assessment.

Left ventricular remodeling is a mechanism common to various cardiovascular diseases affecting myocardial morphology. It can be often overlooked in clinical practice since the parameters routinely employed in the diagnostic process (e.g.

Development of a Deep-Learning Pipeline to Recognize and Characterize Macrophages in Colo-Rectal Liver Metastasis.

Quantitative analysis of Tumor Microenvironment (TME) provides prognostic and predictive information in several human cancers but, with few exceptions, it is not performed in daily clinical practice since it is extremely time-consuming. We recently showed that the morphology of Tumor Associated Macrophages (TAMs) correlates with outcome in patients with Colo-Rectal Liver Metastases (CLM). However, as for other TME components, recognizing and characterizing hundreds of TAMs in a single histopathological slide is unfeasible.

A faster approach to ECG analysis in emergency situations.

Every day, a substantial number of people need to be treated in emergencies and these situations imply a short timeline. Especially concerning heart abnormalities, the time factor is very important. Therefore, we propose a full-stack system for faster and cheaper ECG taking aimed at paramedics, to enhance Emergency Medical Service (EMS) response time.

GPU-accelerated Chemical Similarity Assessment for Large Scale Databases.

The assessment of chemical similarity between molecules is a basic operation in chemoinformatics, a computational area concerning with the manipulation of chemical structural information. Comparing molecules is the basis for a wide range of applications such as searching in chemical databases, training prediction models for virtual screening or aggregating clusters of similar compounds. However, currently available multimillion databases represent a challenge for conventional chemoinformatics algorithms raising the necessity for faster similarity methods.