Pain Management

Efficient Multimodal 3D Object Detector via Instance-Level Contrastive Distillation

Multimodal 3D object detectors leverage the strengths of both geometry-aware LiDAR point clouds and semantically rich RGB images to enhance detection performance. However, the inherent heterogeneity between these modalities, including unbalanced convergence and modal misalignment, poses significant challenges. Meanwhile, the large size of the detection-oriented feature also constrains existing f...

Towards Scalable Foundation Model for Multi-modal and Hyperspectral Geospatial Data

Geospatial raster data, such as that collected by satellite-based imaging systems at different times and spectral bands, hold immense potential for enabling a wide range of high-impact applications. This potential stems from the rich information that is spatially and temporally contextualized across multiple channels and sensing modalities. Recent work has adapted existing self-supervised learni...

Brain Effective Connectivity Estimation via Fourier Spatiotemporal Attention

Estimating brain effective connectivity (EC) from functional magnetic resonance imaging (fMRI) data can aid in comprehending the neural mechanisms u...

Readability, reliability and quality of responses generated by ChatGPT, gemini, and perplexity for the most frequently asked questions about pain.

It is clear that artificial intelligence-based chatbots will be popular applications in the field of healthcare in the near future. It is known that m...

Prototype-Guided Cross-Modal Knowledge Enhancement for Adaptive Survival Prediction

Histo-genomic multimodal survival prediction has garnered growing attention for its remarkable model performance and potential contributions to prec...

dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis

Federated learning has wide applications in the medical field. It enables knowledge sharing among different healthcare institutes while protecting p...

BioSerenity-E1: a self-supervised EEG model for medical applications

Electroencephalography (EEG) serves as an essential diagnostic tool in neurology; however, its accurate manual interpretation is a time-intensive pr...

Reference-Free 3D Reconstruction of Brain Dissection Photographs with Machine Learning

Correlation of neuropathology with MRI has the potential to transfer microscopic signatures of pathology to invivo scans. Recently, a classical regi...

Astrea: A MOE-based Visual Understanding Model with Progressive Alignment

Vision-Language Models (VLMs) based on Mixture-of-Experts (MoE) architectures have emerged as a pivotal paradigm in multimodal understanding, offeri...

OmniMamba: Efficient and Unified Multimodal Understanding and Generation via State Space Models

Recent advancements in unified multimodal understanding and visual generation (or multimodal generation) models have been hindered by their quadrati...

ComicsPAP: understanding comic strips by picking the correct panel

Large multimodal models (LMMs) have made impressive strides in image captioning, VQA, and video comprehension, yet they still struggle with the intr...

EnergyFormer: Energy Attention with Fourier Embedding for Hyperspectral Image Classification

Hyperspectral imaging (HSI) provides rich spectral-spatial information across hundreds of contiguous bands, enabling precise material discrimination...

MPTSNet: Integrating Multiscale Periodic Local Patterns and Global Dependencies for Multivariate Time Series Classification

Multivariate Time Series Classification (MTSC) is crucial in extensive practical applications, such as environmental monitoring, medical EEG analysi...

Leveraging Semantic Type Dependencies for Clinical Named Entity Recognition

Previous work on clinical relation extraction from free-text sentences leveraged information about semantic types from clinical knowledge bases as a...

New multimodal similarity measure for image registration via modeling local functional dependence with linear combination of learned basis functions

The deformable registration of images of different modalities, essential in many medical imaging applications, remains challenging. The main challen...

Electrocardiogram-based machine learning for risk stratification of patients with suspected acute coronary syndrome.

BACKGROUND AND AIMS: The importance of risk stratification in patients with chest pain extends beyond diagnosis and immediate treatment. This study so...

CoServe: Efficient Collaboration-of-Experts (CoE) Model Inference with Limited Memory

Large language models like GPT-4 are resource-intensive, but recent advancements suggest that smaller, specialized experts can outperform the monoli...

COMMA: Coordinate-aware Modulated Mamba Network for 3D Dispersed Vessel Segmentation

Accurate segmentation of 3D vascular structures is essential for various medical imaging applications. The dispersed nature of vascular structures l...

SSNet: Saliency Prior and State Space Model-based Network for Salient Object Detection in RGB-D Images

Salient object detection (SOD) in RGB-D images is an essential task in computer vision, enabling applications in scene understanding, robotics, and ...

Bomfather: An eBPF-based Kernel-level Monitoring Framework for Accurate Identification of Unknown, Unused, and Dynamically Loaded Dependencies in Modern Software Supply Chains

Inaccuracies in conventional dependency-tracking methods frequently undermine the security and integrity of modern software supply chains. This pape...