Critical Care

Generalized probabilistic canonical correlation analysis for multi-modal data integration with full or partial observations

Background: The integration and analysis of multi-modal data are increasingly essential across various domains including bioinformatics. As the volume and complexity of such data grow, there is a pressing need for computational models that not only integrate diverse modalities but also leverage their complementary information to improve clustering accuracy and insights, especially when dealing w...

Respiratory Inhaler Sound Event Classification Using Self-Supervised Learning

Asthma is a chronic respiratory condition that affects millions of people worldwide. While this condition can be managed by administering controller medications through handheld inhalers, clinical studies have shown low adherence to the correct inhaler usage technique. Consequently, many patients may not receive the full benefit of their medication. Automated classification of inhaler sounds has...

FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcare

Federated Learning (FL) has emerged as an effective solution for multi-institutional collaborations without sharing patient data, offering a range o...

Do We Really Need Curated Malicious Data for Safety Alignment in Multi-modal Large Language Models?

Multi-modal large language models (MLLMs) have made significant progress, yet their safety alignment remains limited. Typically, current open-source...

Enhancing Multi-task Learning Capability of Medical Generalist Foundation Model via Image-centric Multi-annotation Data

The emergence of medical generalist foundation models has revolutionized conventional task-specific model development paradigms, aiming to better ha...

CUT: Pruning Pre-Trained Multi-Task Models into Compact Models for Edge Devices

Multi-task learning has garnered widespread attention in the industry due to its efficient data utilization and strong generalization capabilities, ...

The Structural Safety Generalization Problem

LLM jailbreaks are a widespread safety challenge. Given this problem has not yet been tractable, we suggest targeting a key failure mechanism: the f...

Reconstructing Sepsis Trajectories from Clinical Case Reports using LLMs: the Textual Time Series Corpus for Sepsis

Clinical case reports and discharge summaries may be the most complete and accurate summarization of patient encounters, yet they are finalized, i.e...

Inferring Outcome Means of Exponential Family Distributions Estimated by Deep Neural Networks

While deep neural networks (DNNs) are widely used for prediction, inference on DNN-estimated subject-specific means for categorical or exponential f...

Accurate Diagnosis of Respiratory Viruses Using an Explainable Machine Learning with Mid-Infrared Biomolecular Fingerprinting of Nasopharyngeal Secretions

Accurate identification of respiratory viruses (RVs) is critical for outbreak control and public health. This study presents a diagnostic system tha...

Multi-modal and Multi-view Fundus Image Fusion for Retinopathy Diagnosis via Multi-scale Cross-attention and Shifted Window Self-attention

The joint interpretation of multi-modal and multi-view fundus images is critical for retinopathy prevention, as different views can show the complet...

Multi-Modal Brain Tumor Segmentation via 3D Multi-Scale Self-attention and Cross-attention

Due to the success of CNN-based and Transformer-based models in various computer vision tasks, recent works study the applicability of CNN-Transform...

Marmot: Multi-Agent Reasoning for Multi-Object Self-Correcting in Improving Image-Text Alignment

While diffusion models excel at generating high-quality images, they often struggle with accurate counting, attributes, and spatial relationships in...

Going beyond explainability in multi-modal stroke outcome prediction models

Aim: This study aims to enhance interpretability and explainability of multi-modal prediction models integrating imaging and tabular patient data. ...

Beyond Single-Turn: A Survey on Multi-Turn Interactions with Large Language Models

Recent advancements in large language models (LLMs) have revolutionized their ability to handle single-turn tasks, yet real-world applications deman...

Transformer representation learning is necessary for dynamic multi-modal physiological data on small-cohort patients

Postoperative delirium (POD), a severe neuropsychiatric complication affecting nearly 50% of high-risk surgical patients, is defined as an acute dis...

Multi-resolution Score-Based Variational Graphical Diffusion for Causal Disaster System Modeling and Inference

Complex systems with intricate causal dependencies challenge accurate prediction. Effective modeling requires precise physical process representatio...

YaleNLP @ PerAnsSumm 2025: Multi-Perspective Integration via Mixture-of-Agents for Enhanced Healthcare QA Summarization

Automated summarization of healthcare community question-answering forums is challenging due to diverse perspectives presented across multiple user ...

Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detection

With more well-performing anomaly detection methods proposed, many of the single-view tasks have been solved to a relatively good degree. However, r...

ConsDreamer: Advancing Multi-View Consistency for Zero-Shot Text-to-3D Generation

Recent advances in zero-shot text-to-3D generation have revolutionized 3D content creation by enabling direct synthesis from textual descriptions. W...