Intensivists

Unpaired Translation of Chest X-ray Images for Lung Opacity Diagnosis via Adaptive Activation Masks and Cross-Domain Alignment

Chest X-ray radiographs (CXRs) play a pivotal role in diagnosing and monitoring cardiopulmonary diseases. However, lung opacities in CXRs frequently obscure anatomical structures, impeding clear identification of lung borders and complicating the localization of pathology. This challenge significantly hampers segmentation accuracy and precise lesion identification, which are crucial for diagnosi...

How to RETIRE Tabular Data in Favor of Discrete Digital Signal Representation

The successes achieved by deep neural networks in computer vision tasks have led in recent years to the emergence of a new research area dubbed Multi-Dimensional Encoding (MDE). Methods belonging to this family aim to transform tabular data into a homogeneous form of discrete digital signals (images) to apply convolutional networks to initially unsuitable problems. Despite the successive emergin...

MotionDiff: Training-free Zero-shot Interactive Motion Editing via Flow-assisted Multi-view Diffusion

Generative models have made remarkable advancements and are capable of producing high-quality content. However, performing controllable editing with...

NeuroSep-CP-LCB: A Deep Learning-based Contextual Multi-armed Bandit Algorithm with Uncertainty Quantification for Early Sepsis Prediction

In critical care settings, timely and accurate predictions can significantly impact patient outcomes, especially for conditions like sepsis, where e...

MMAIF: Multi-task and Multi-degradation All-in-One for Image Fusion with Language Guidance

Image fusion, a fundamental low-level vision task, aims to integrate multiple image sequences into a single output while preserving as much informat...

Reducing False Ventricular Tachycardia Alarms in ICU Settings: A Machine Learning Approach

False arrhythmia alarms in intensive care units (ICUs) are a significant challenge, contributing to alarm fatigue and potentially compromising patie...

Predicting Cardiopulmonary Exercise Testing Outcomes in Congenital Heart Disease Through Multi-modal Data Integration and Geometric Learning

Cardiopulmonary exercise testing (CPET) provides a comprehensive assessment of functional capacity by measuring key physiological variables includin...

MMR: A Large-scale Benchmark Dataset for Multi-target and Multi-granularity Reasoning Segmentation

The fusion of Large Language Models with vision models is pioneering new possibilities in user-interactive vision-language tasks. A notable applicat...

Multi-modal Time Series Analysis: A Tutorial and Survey

Multi-modal time series analysis has recently emerged as a prominent research area in data mining, driven by the increasing availability of diverse ...

NuPlanQA: A Large-Scale Dataset and Benchmark for Multi-View Driving Scene Understanding in Multi-Modal Large Language Models

Recent advances in multi-modal large language models (MLLMs) have demonstrated strong performance across various domains; however, their ability to ...

MAP: Multi-user Personalization with Collaborative LLM-powered Agents

The widespread adoption of Large Language Models (LLMs) and LLM-powered agents in multi-user settings underscores the need for reliable, usable meth...

A Transformer-based survival model for prediction of all-cause mortality in heart failure patients: a multi-cohort study

We developed and validated TRisk, a Transformer-based AI model predicting 36-month mortality in heart failure patients by analysing temporal patient...

Multi-output Classification for Compound Fault Diagnosis in Motor under Partially Labeled Target Domain

This study presents a novel multi-output classification (MOC) framework designed for domain adaptation in fault diagnosis, addressing challenges pos...

SpaceSeg: A High-Precision Intelligent Perception Segmentation Method for Multi-Spacecraft On-Orbit Targets

With the continuous advancement of human exploration into deep space, intelligent perception and high-precision segmentation technology for on-orbit...

A Multi-Modal Federated Learning Framework for Remote Sensing Image Classification

Federated learning (FL) enables the collaborative training of deep neural networks across decentralized data archives (i.e., clients) without sharin...

DAMM-Diffusion: Learning Divergence-Aware Multi-Modal Diffusion Model for Nanoparticles Distribution Prediction

The prediction of nanoparticles (NPs) distribution is crucial for the diagnosis and treatment of tumors. Recent studies indicate that the heterogene...

An Iterative, User-Centered Design of a Clinical Decision Support System for Critical Care Assessments: Co-Design Sessions with ICU Clinical Providers

This study reports the findings of qualitative interview sessions conducted with ICU clinicians for the co-design of a system user interface of an a...

POINT: a web-based platform for pharmacological investigation enhanced by multi-omics networks and knowledge graphs

Network pharmacology (NP) explores pharmacological mechanisms through biological networks. Multi-omics data enable multi-layer network construction ...

Learning a Unified Degradation-aware Representation Model for Multi-modal Image Fusion

All-in-One Degradation-Aware Fusion Models (ADFMs), a class of multi-modal image fusion models, address complex scenes by mitigating degradations fr...

Enhanced Multi-Tuple Extraction for Alloys: Integrating Pointer Networks and Augmented Attention

Extracting high-quality structured information from scientific literature is crucial for advancing material design through data-driven methods. Desp...