Latest AI and machine learning research in critical care for healthcare professionals.
Polysomnographic sleep analysis is the standard clinical method to accurately diagnose and treat sleep disorders. It is an intricate process which involves the manual identification, classification, and location of multiple sleep event patterns. This is complex, for which identification of different types of events involves focusing on different subsets of signals, resulting on an iterative time...
Functional peptides are short amino acid fragments that have a wide range of beneficial functions for living organisms. The majority of previous studies have focused on mono-functional peptides, but an increasing number of multi-functional peptides have been discovered. Although there have been enormous experimental efforts to assay multi-functional peptides, only a small portion of millions of kn...
In volume-to-volume translations in medical images, existing models often struggle to capture the inherent volumetric distribution using 3D voxelspa...
Multi-modal language model has made advanced progress in vision and audio, but still faces significant challenges in dealing with complex reasoning ...
The recent advancement of Multimodal Large Language Models (MLLMs) has significantly improved their fine-grained perception of single images and gen...
In scenarios where multiple decision-makers operate within a common decision space, each focusing on their own multi-objective optimization problem ...
Accurate Intensive Care Unit (ICU) outcome prediction is critical for improving patient treatment quality and ICU resource allocation. Existing rese...
Acknowledging the effects of outdoor air pollution, the literature inadequately addresses indoor air pollution's impacts. Despite daily health risks...
This work deals with the challenge of learning and reasoning over multi-modal multi-hop question answering (QA). We propose a graph reasoning networ...
Current medical image segmentation approaches have limitations in deeply exploring multi-scale information and effectively combining local detail te...
Vision-Language Models (VLMs) excel at understanding single images, aided by high-quality instruction datasets. However, multi-image reasoning remai...
Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interfaces (BCIs) facilitate high-throughput target image detection by identifying even...
Large language models and vision transformers have demonstrated impressive zero-shot capabilities, enabling significant transferability in downstrea...
Systematic literature reviews and meta-analyses are essential for synthesizing research insights, but they remain time-intensive and labor-intensive...
Large Language Models (LLMs) excel at a wide range of tasks, but adapting them to new data, particularly for personalized applications, poses signif...
Intracerebral hemorrhage (ICH) is a life-risking condition characterized by bleeding within the brain parenchyma. ICU readmission in ICH patients is...
Source-Free Domain Generalization (SFDG) aims to develop a model that performs on unseen domains without relying on any source domains. However, the...
Aging clocks have emerged as the primary tools for measuring biological aging and have been developed for a wide range of single-omic measurements. Ep...
Precise evaluation of immune status is critical for managing diseases such as sepsis, in which the immune system transitions between hyper-inflammator...
Foundation models offer new opportunities to capture cellular behavior from large-scale single-cell data. However, their development has been greatly ...