Semi-supervised contour-driven broad learning system for autonomous segmentation of concealed prohibited baggage items.

With the exponential rise in global air traffic, ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation security. Although X-ray baggage monitoring is now standard, manual screening has several limitations, including the propensity for errors, and raises concerns about passenger privacy. To address these drawbacks, researchers have leveraged recent advances in deep learning to design threat-segmentation frameworks.

RLAD: A Reliable Hippo-guided Multi-task Model for Alzheimer's Disease Diagnosis.

Early diagnosis of Alzheimer's disease (AD) is crucial for its prevention, and hippocampal atrophy is a significant lesion for early diagnosis. The current DL-based AD diagnosis methods only focus on either AD classification or hippocampus segmentation independently, neglecting the correlation between the two tasks and lacking pathological interpretability. To address this issue, we propose a Reliable Hippo-guided Learning model for Alzheimer's Disease diagnosis (RLAD), which employs multi-task learning for AD classification as a main task supplemented by hippocampus segmentation.

MCTformer+: Multi-Class Token Transformer for Weakly Supervised Semantic Segmentation.

This paper proposes a novel transformer-based framework to generate accurate class-specific object localization maps for weakly supervised semantic segmentation (WSSS). Leveraging the insight that the attended regions of the one-class token in the standard vision transformer can generate class-agnostic localization maps, we investigate the transformer's capacity to capture class-specific attention for class-discriminative object localization by learning multiple class tokens. We present the Multi-Class Token transformer, which incorporates multiple class tokens to enable class-aware interactions with patch tokens.

Predicting multifaceted risks using machine learning in atrial fibrillation: insights from GLORIA-AF study.

Aims: Patients with atrial fibrillation (AF) have a higher risk of ischaemic stroke and death. While anticoagulants are effective at reducing these risks, they increase the risk of bleeding. Current clinical risk scores only perform modestly in predicting adverse outcomes, especially for the outcome of death.

Region Aware Video Object Segmentation With Deep Motion Modeling.

Current semi-supervised video object segmentation (VOS) methods often employ the entire features of one frame to predict object masks and update memory. This introduces significant redundant computations. To reduce redundancy, we introduce a Region Aware Video Object Segmentation (RAVOS) approach, which predicts regions of interest (ROIs) for efficient object segmentation and memory storage.

Auxiliary Tasks Enhanced Dual-Affinity Learning for Weakly Supervised Semantic Segmentation.

Most existing weakly supervised semantic segmentation (WSSS) methods rely on class activation mapping (CAM) to extract coarse class-specific localization maps using image-level labels. Prior works have commonly used an off-line heuristic thresholding process that combines the CAM maps with off-the-shelf saliency maps produced by a general pretrained saliency model to produce more accurate pseudo-segmentation labels. We propose AuxSegNet + , a weakly supervised auxiliary learning framework to explore the rich information from these saliency maps and the significant intertask correlation between saliency detection and semantic segmentation.

Applications of natural language processing at emergency department triage: A narrative review.

Introduction: Natural language processing (NLP) uses various computational methods to analyse and understand human language, and has been applied to data acquired at Emergency Department (ED) triage to predict various outcomes. The objective of this scoping review is to evaluate how NLP has been applied to data acquired at ED triage, assess if NLP based models outperform humans or current risk stratification techniques when predicting outcomes, and assess if incorporating free-text improve predictive performance of models when compared to predictive models that use only structured data.

Methods: All English language peer-reviewed research that applied an NLP technique to free-text obtained at ED triage was eligible for inclusion.

DNABERT-based explainable lncRNA identification in plant genome assemblies.

Long non-coding ribonucleic acids (lncRNAs) have been shown to play an important role in plant gene regulation, involving both epigenetic and transcript regulation. LncRNAs are transcripts longer than 200 nucleotides that are not translated into functional proteins but can be translated into small peptides. Machine learning models have predominantly used transcriptome data with manually defined features to detect lncRNAs, however, they often underrepresent the abundance of lncRNAs and can be biased in their detection.

Silencing of genes by promoter hypermethylation shapes tumor microenvironment and resistance to immunotherapy in clear-cell renal cell carcinomas.

The efficacy of immune checkpoint inhibitors varies in clear-cell renal cell carcinoma (ccRCC), with notable primary resistance among patients. Here, we integrate epigenetic (DNA methylation) and transcriptome data to identify a ccRCC subtype characterized by cancer-specific promoter hypermethylation and epigenetic silencing of Polycomb targets. We develop and validate an index of methylation-based epigenetic silencing (iMES) that predicts primary resistance to immune checkpoint inhibition (ICI) in the BIONIKK trial.

Biweekly vs Triweekly Cabazitaxel in Older Patients With Metastatic Castration-Resistant Prostate Cancer: The CABASTY Phase 3 Randomized Clinical Trial.

Importance: Many patients 65 years or older with metastatic castration-resistant prostate cancer (mCRPC) are denied taxane chemotherapy because this treatment is considered unsuitable.

Objective: To determine whether biweekly cabazitaxel (CBZ), 16 mg/m2 (biweekly CBZ16), plus prophylactic granulocyte colony-stimulating factor (G-CSF) at each cycle reduces the risk of grade 3 or higher neutropenia and/or neutropenic complications (eg, febrile neutropenia, neutropenic infection, or sepsis) compared with triweekly CBZ, 25 mg/m2 (triweekly CBZ25), plus G-CSF (standard regimen).

Design, Setting, And Participants: A total of 196 patients 65 years or older with progressive mCRPC were enrolled in this prospective phase 3 randomized clinical trial conducted in France (18 centers) and Germany (7 centers) between May 5, 2017, and January 7, 2021.

Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation.

Atrial fibrillation arises mainly due to abnormalities in the cardiac conduction system and is associated with anatomical remodeling of the atria and the pulmonary veins. Cardiovascular imaging techniques, such as echocardiography, computed tomography, and magnetic resonance imaging, are crucial in the management of atrial fibrillation, as they not only provide anatomical context to evaluate structural alterations but also help in determining treatment strategies. However, interpreting these images requires significant human expertise.

Western Australian medical students' attitudes towards artificial intelligence in healthcare.

Introduction: Surveys conducted internationally have found widespread interest in artificial intelligence (AI) amongst medical students. No similar surveys have been conducted in Western Australia (WA) and it is not known how medical students in WA feel about the use of AI in healthcare or their understanding of AI. We aim to assess WA medical students' attitudes towards AI in general, AI in healthcare, and the inclusion of AI education in the medical curriculum.

Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification.

Cross-resolution person re-identification (CRReID) is a challenging and practical problem that involves matching low-resolution (LR) query identity images against high-resolution (HR) gallery images. Query images often suffer from resolution degradation due to the different capturing conditions from real-world cameras. State-of-the-art solutions for CRReID either learn a resolution-invariant representation or adopt a super-resolution (SR) module to recover the missing information from the LR query.

Deep learning model to predict exercise stress test results: Optimizing the diagnostic test selection strategy and reduce wastage in suspected coronary artery disease patients.

Background: Cardiac exercise stress testing (EST) offers a non-invasive way in the management of patients with suspected coronary artery disease (CAD). However, up to 30% EST results are either inconclusive or non-diagnostic, which results in significant resource wastage. Our aim was to build machine learning (ML) based models, using patients demographic (age, sex) and pre-test clinical information (reason for performing test, medications, blood pressure, heart rate, and resting electrocardiogram), capable of predicting EST results beforehand including those with inconclusive or non-diagnostic results.

Inflating 2D convolution weights for efficient generation of 3D medical images.

Background And Objective: The generation of three-dimensional (3D) medical images has great application potential since it takes into account the 3D anatomical structure. Two problems prevent effective training of a 3D medical generative model: (1) 3D medical images are expensive to acquire and annotate, resulting in an insufficient number of training images, and (2) a large number of parameters are involved in 3D convolution.

Methods: We propose a novel GAN model called 3D Split&Shuffle-GAN.

Mesenchymal-like Tumor Cells and Myofibroblastic Cancer-Associated Fibroblasts Are Associated with Progression and Immunotherapy Response of Clear Cell Renal Cell Carcinoma.

Unlabelled: Immune checkpoint inhibitors (ICI) represent the cornerstone for the treatment of patients with metastatic clear cell renal cell carcinoma (ccRCC). Despite a favorable response for a subset of patients, others experience primary progressive disease, highlighting the need to precisely understand the plasticity of cancer cells and their cross-talk with the microenvironment to better predict therapeutic response and personalize treatment. Single-cell RNA sequencing of ccRCC at different disease stages and normal adjacent tissue (NAT) from patients identified 46 cell populations, including 5 tumor subpopulations, characterized by distinct transcriptional signatures representing an epithelial-to-mesenchymal transition gradient and a novel inflamed state.

Transport of pilgrims during Hajj: Evidence from a discrete event simulation study.

Hajj, the Muslim pilgrimage, is a large mass gathering event that involves performing rituals at several sites on specific days and times in a fixed order, thereby requiring transport of pilgrims between sites. For the past two decades, Hajj transport has relied on conventional and shuttle buses, train services, and pilgrims walking along pedestrian routes that link these sites. To ensure smooth and efficient transport during Hajj, specific groups of pilgrims are allocated with the cooperation of Hajj authorities to specific time windows, modes, and routes.

Higher Order Polynomial Transformer for Fine-Grained Freezing of Gait Detection.

Freezing of Gait (FoG) is a common symptom of Parkinson's disease (PD), manifesting as a brief, episodic absence, or marked reduction in walking, despite a patient's intention to move. Clinical assessment of FoG events from manual observations by experts is both time-consuming and highly subjective. Therefore, machine learning-based FoG identification methods would be desirable.

An Enhancer Demethylator Phenotype Converged to Immune Dysfunction and Resistance to Immune Checkpoint Inhibitors in Clear-Cell Renal Cell Carcinomas.

Purpose: Immune checkpoint inhibitors (ICI) have revolutionized the treatment of patients with clear-cell renal cell carcinomas (ccRCC). Although analyses of transcriptome, genetic alterations, and the tumor microenvironment (TME) have shed light into mechanisms of response and resistance to these agents, the role of epigenetic alterations in this process remains fully unknown.

Experimental Design: We investigated the methylome of six ccRCC cohorts as well as one cell line dataset.

Real-world Treatment Sequencing in Patients with Metastatic Castration-resistant Prostate Cancer: Results from the Prospective, International, Observational Prostate Cancer Registry.

Background: Prostate cancer has a multifaceted treatment pattern. Evidence is lacking for optimal treatment sequences for metastatic castration-resistant prostate cancer (mCRPC).

Objective: To increase the understanding of real-world treatment pathways and outcomes in patients with mCRPC.

Spatio-Temporal Graph Representation Learning for Fraudster Group Detection.

Motivated by potential financial gain, companies may hire fraudster groups to write fake reviews to either demote competitors or promote their own businesses. Such groups are considerably more successful in misleading customers, as people are more likely to be influenced by the opinion of a large group. To detect such groups, a common model is to represent fraudster groups' static networks, consequently overlooking the longitudinal behavior of a reviewer, thus, the dynamics of coreview relations among reviewers in a group.

Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation.

Background: Appropriate anticoagulant therapy for patients with atrial fibrillation (AF) requires assessment of stroke and bleeding risks. However, risk stratification schemas such as CHADS-VASc and HAS-BLED have modest predictive capacity for patients with AF. Multilabel machine learning (ML) techniques may improve predictive performance and support decision-making for anticoagulant therapy.

Untrained Neural Network Priors for Inverse Imaging Problems: A Survey.

In recent years, advancements in machine learning (ML) techniques, in particular, deep learning (DL) methods have gained a lot of momentum in solving inverse imaging problems, often surpassing the performance provided by hand-crafted approaches. Traditionally, analytical methods have been used to solve inverse imaging problems such as image restoration, inpainting, and superresolution. Unlike analytical methods for which the problem is explicitly defined and the domain knowledge is carefully engineered into the solution, DL models do not benefit from such prior knowledge and instead make use of large datasets to predict an unknown solution to the inverse problem.