Diagnosing Helicobacter pylori using autoencoders and limited annotations through anomalous staining patterns in IHC whole slide images.

Purpose: This work addresses the detection of Helicobacter pylori (H. pylori) in histological images with immunohistochemical staining. This analysis is a time-demanding task, currently done by an expert pathologist that visually inspects the samples.

Leveraging U-Net and selective feature extraction for land cover classification using remote sensing imagery.

In this study, we explore an enhancement to the U-Net architecture by integrating SK-ResNeXt as the encoder for Land Cover Classification (LCC) tasks using Multispectral Imaging (MSI). SK-ResNeXt introduces cardinality and adaptive kernel sizes, allowing U-Net to better capture multi-scale features and adjust more effectively to variations in spatial resolution, thereby enhancing the model's ability to segment complex land cover types. We evaluate this approach using the Five-Billion-Pixels dataset, composed of 150 large-scale RGB-NIR images and over 5 billion labeled pixels across 24 categories.

Breast radiotherapy planning: A decision-making framework using deep learning.

Background: Effective breast cancer treatment planning requires balancing tumor control while minimizing radiation exposure to healthy tissues. Choosing between intensity-modulated radiation therapy (IMRT) and three-dimensional conformal radiation therapy (3D-CRT) remains pivotal, influenced by patient anatomy and dosimetric constraints.

Purpose: This study aims to develop a decision-making framework utilizing deep learning to predict dose distributions, aiding in the selection of optimal treatment techniques.

Virtual airways heatmaps to optimize point of entry location in lung biopsy planning systems.

Purpose: We present a virtual model to optimize point of entry (POE) in lung biopsy planning systems. Our model allows to compute the quality of a biopsy sample taken from potential POE, taking into account the margin of error that arises from discrepancies between the orientation in the planning simulation and the actual orientation during the operation. Additionally, the study examines the impact of the characteristics of the lesion.

Enhanced Artificial Intelligence Methods for Liver Steatosis Assessment Using Machine Learning and Color Image Processing: Liver Color Project.

Background: The use of livers with significant steatosis is associated with worse transplantation outcomes. Brain death donor liver acceptance is mostly based on subjective surgeon assessment of liver appearance, since steatotic livers acquire a yellowish tone. The aim of this study was to develop a rapid, robust, accurate, and cost-effective method to assess liver steatosis.

LiverColor: An Artificial Intelligence Platform for Liver Graft Assessment.

Hepatic steatosis, characterized by excess fat in the liver, is the main reason for discarding livers intended for transplantation due to its association with increased postoperative complications. The current gold standard for evaluating hepatic steatosis is liver biopsy, which, despite its accuracy, is invasive, costly, slow, and not always feasible during liver procurement. Consequently, surgeons often rely on subjective visual assessments based on the liver's colour and texture, which are prone to errors and heavily depend on the surgeon's experience.

Radiomics and Clinical Data for the Diagnosis of Incidental Pulmonary Nodules and Lung Cancer Screening: Radiolung Integrative Predictive Model.

Introduction: Early diagnosis of lung cancer (LC) is crucial to improve survival rates. Radiomics models hold promise for enhancing LC diagnosis. This study assesses the impact of integrating a clinical and a radiomic model based on deep learning to predict the malignancy of pulmonary nodules (PN).

Image Quality Assessment Tool for Conventional and Dynamic Magnetic Resonance Imaging Acquisitions.

Image quality assessment of magnetic resonance imaging (MRI) data is an important factor not only for conventional diagnosis and protocol optimization but also for fairness, trustworthiness, and robustness of artificial intelligence (AI) applications, especially on large heterogeneous datasets. Information on image quality in multi-centric studies is important to complement the contribution profile from each data node along with quantity information, especially when large variability is expected, and certain acceptance criteria apply. The main goal of this work was to present a tool enabling users to assess image quality based on both subjective criteria as well as objective image quality metrics used to support the decision on image quality based on evidence.

Deep learning-based model for difficult transfemoral access prediction compared with human assessment in stroke thrombectomy.

Background: In mechanical thrombectomy (MT), extracranial vascular tortuosity is among the main determinants of procedure duration and success. Currently, no rapid and reliable method exists to identify the anatomical features precluding fast and stable access to the cervical vessels.

Methods: A retrospective sample of 513 patients were included in this study.

Artificial Intelligence for breast cancer detection: Technology, challenges, and prospects.

Purpose: This review provides an overview of the current state of artificial intelligence (AI) technology for automated detection of breast cancer in digital mammography (DM) and digital breast tomosynthesis (DBT). It aims to discuss the technology, available AI systems, and the challenges faced by AI in breast cancer screening.

Methods: The review examines the development of AI technology in breast cancer detection, focusing on deep learning (DL) techniques and their differences from traditional computer-aided detection (CAD) systems.

Generalizable disease detection using model ensemble on chest X-ray images.

In the realm of healthcare, the demand for swift and precise diagnostic tools has been steadily increasing. This study delves into a comprehensive performance analysis of three pre-trained convolutional neural network (CNN) architectures: ResNet50, DenseNet121, and Inception-ResNet-v2. To ensure the broad applicability of our approach, we curated a large-scale dataset comprising a diverse collection of chest X-ray images, that included both positive and negative cases of COVID-19.

EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment.

High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios.

Bayesian DivideMix++ for Enhanced Learning with Noisy Labels.

Leveraging inexpensive and human intervention-based annotating methodologies, such as crowdsourcing and web crawling, often leads to datasets with noisy labels. Noisy labels can have a detrimental impact on the performance and generalization of deep neural networks. Robust models that are able to handle and mitigate the effect of these noisy labels are thus essential.

The role of post-dilatation in carotid MicroNet-covered stent implantation, evaluated using 3D cone-beam CT angiography.

Background: This study aims to assess the role and safety of post-dilatation in protected carotid artery stenting (PCAS) using the new MicroNet-covered 2nd-generation stent assessed by cone beam CT scans.

Methods: From March 2020 to March 2022, patients were enrolled in the study according to CT angiography results based on the following criteria: Evidence of 70% to 99% carotid stenosis in asymptomatic patients and 50% to 99% in symptomatic patients, per the NASCET index. Using a FilterWire EZ™ (Boston Scientific, Natick, MA, USA) embolic protection system (EPS), MicroNet-covered stent PCAS was performed by two interventional radiologists with at least 8 years of experience in endovascular intervention.

Radiomics analysis enhances the diagnostic performance of CMR stress perfusion: a proof-of-concept study using the Dan-NICAD dataset.

Objectives: To assess the feasibility of extracting radiomics signal intensity based features from the myocardium using cardiovascular magnetic resonance (CMR) imaging stress perfusion sequences. Furthermore, to compare the diagnostic performance of radiomics models against standard-of-care qualitative visual assessment of stress perfusion images, with the ground truth stenosis label being defined by invasive Fractional Flow Reserve (FFR) and quantitative coronary angiography.

Methods: We used the Dan-NICAD 1 dataset, a multi-centre study with coronary computed tomography angiography, 1,5 T CMR stress perfusion, and invasive FFR available for a subset of 148 patients with suspected coronary artery disease.

A mechanistic account of visual discomfort.

Much of the neural machinery of the early visual cortex, from the extraction of local orientations to contextual modulations through lateral interactions, is thought to have developed to provide a sparse encoding of contour in natural scenes, allowing the brain to process efficiently most of the visual scenes we are exposed to. Certain visual stimuli, however, cause visual stress, a set of adverse effects ranging from simple discomfort to migraine attacks, and epileptic seizures in the extreme, all phenomena linked with an excessive metabolic demand. The theory of efficient coding suggests a link between excessive metabolic demand and images that deviate from natural statistics.

Curriculum learning-based strategy for low-density archaeological mound detection from historical maps in India and Pakistan.

This paper presents two algorithms for the large-scale automatic detection and instance segmentation of potential archaeological mounds on historical maps. Historical maps present a unique source of information for the reconstruction of ancient landscapes. The last 100 years have seen unprecedented landscape modifications with the introduction and large-scale implementation of mechanised agriculture, channel-based irrigation schemes, and urban expansion to name but a few.

The evolution and ecology of multiple antipredator defences.

Prey seldom rely on a single type of antipredator defence, often using multiple defences to avoid predation. In many cases, selection in different contexts may favour the evolution of multiple defences in a prey. However, a prey may use multiple defences to protect itself during a single predator encounter.

Gender Bias When Using Artificial Intelligence to Assess Anorexia Nervosa on Social Media: Data-Driven Study.

Background: Social media sites are becoming an increasingly important source of information about mental health disorders. Among them, eating disorders are complex psychological problems that involve unhealthy eating habits. In particular, there is evidence showing that signs and symptoms of anorexia nervosa can be traced in social media platforms.

MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images.

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on the myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e.

Outcomes of multifarious selection on the evolution of visual signals.

Multifarious sources of selection shape visual signals and can produce phenotypic divergence. Theory predicts that variance in warning signals should be minimal due to purifying selection, yet polymorphism is abundant. While in some instances divergent signals can evolve into discrete morphs, continuously variable phenotypes are also encountered in natural populations.

Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models.

Semantic image segmentation is a core task for autonomous driving, which is performed by deep models. Since training these models draws to a curse of human-based image labeling, the use of synthetic images with automatically generated labels together with unlabeled real-world images is a promising alternative. This implies addressing an unsupervised domain adaptation (UDA) problem.