Care of terminally ill / Palliative care

An end-to-end mass spectrometry data classification model with a unified architecture.

Mass spectrometry, known for its high sensitivity, selectivity, rich structural information, and rapid analysis capabilities, is widely used in disease diagnosis and bioanalysis. Despite progress in classification methods/tools for data collection in the past decade, problems such as complex data processing, weak model characterization, and large interbatch differences persist. To address these pr...

Dc-EEMF: Pushing depth-of-field limit of photoacoustic microscopy via decision-level constrained learning

Photoacoustic microscopy holds the potential to measure biomarkers' structural and functional status without labels, which significantly aids in comprehending pathophysiological conditions in biomedical research. However, conventional optical-resolution photoacoustic microscopy (OR-PAM) is hindered by a limited depth-of-field (DoF) due to the narrow depth range focused on a Gaussian beam. Conseq...

Robust and Annotation-Free Wound Segmentation on Noisy Real-World Pressure Ulcer Images: Towards Automated DESIGN-R\textsuperscript{\textregistered} Assessment

Purpose: Accurate wound segmentation is essential for automated DESIGN-R scoring. However, existing models such as FUSegNet, which are trained prima...

Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction Networks for Single-Pixel Imaging

Deep-unrolling and plug-and-play (PnP) approaches have become the de-facto standard solvers for single-pixel imaging (SPI) inverse problem. PnP appr...

3DGS Compression with Sparsity-guided Hierarchical Transform Coding

3D Gaussian Splatting (3DGS) has gained popularity for its fast and high-quality rendering, but it has a very large memory footprint incurring high ...

Test-Time Alignment of Discrete Diffusion Models with Sequential Monte Carlo

Discrete diffusion models have become highly effective across various domains. However, real-world applications often require the generative process...

RAG-Zeval: Towards Robust and Interpretable Evaluation on RAG Responses through End-to-End Rule-Guided Reasoning

Robust evaluation is critical for deploying trustworthy retrieval-augmented generation (RAG) systems. However, current LLM-based evaluation framewor...

Large-Area Fabrication-aware Computational Diffractive Optics

Differentiable optics, as an emerging paradigm that jointly optimizes optics and (optional) image processing algorithms, has made innovative optical...

EaqVLA: Encoding-aligned Quantization for Vision-Language-Action Models

With the development of Embodied Artificial intelligence, the end-to-end control policy such as Vision-Language-Action (VLA) model has become the ma...

Visual Product Graph: Bridging Visual Products And Composite Images For End-to-End Style Recommendations

Retrieving semantically similar but visually distinct contents has been a critical capability in visual search systems. In this work, we aim to tack...

Sci-Fi: Symmetric Constraint for Frame Inbetweening

Frame inbetweening aims to synthesize intermediate video sequences conditioned on the given start and end frames. Current state-of-the-art methods m...

BIPNN: Learning to Solve Binary Integer Programming via Hypergraph Neural Networks

Binary (0-1) integer programming (BIP) is pivotal in scientific domains requiring discrete decision-making. As the advance of AI computing, recent w...

GraphAU-Pain: Graph-based Action Unit Representation for Pain Intensity Estimation

Understanding pain-related facial behaviors is essential for digital healthcare in terms of effective monitoring, assisted diagnostics, and treatmen...

MT$^{3}$: Scaling MLLM-based Text Image Machine Translation via Multi-Task Reinforcement Learning

Text Image Machine Translation (TIMT)-the task of translating textual content embedded in images-is critical for applications in accessibility, cros...

I2MoE: Interpretable Multimodal Interaction-aware Mixture-of-Experts

Modality fusion is a cornerstone of multimodal learning, enabling information integration from diverse data sources. However, vanilla fusion methods...

WeedNet: A Foundation Model-Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification

Early identification of weeds is essential for effective management and control, and there is growing interest in automating the process using compu...

Performance of machine learning models for predicting high-severity symptoms in multiple sclerosis.

Current care in multiple sclerosis (MS) primarily relies on infrequently obtained data such as magnetic resonance imaging, clinical laboratory tests o...

ConnectomeDiffuser: Generative AI Enables Brain Network Construction from Diffusion Tensor Imaging

Brain network analysis plays a crucial role in diagnosing and monitoring neurodegenerative disorders such as Alzheimer's disease (AD). Existing appr...

Decoupled Visual Interpretation and Linguistic Reasoning for Math Problem Solving

Current large vision-language models (LVLMs) typically employ a connector module to link visual features with text embeddings of large language mode...

FullFront: Benchmarking MLLMs Across the Full Front-End Engineering Workflow

Front-end engineering involves a complex workflow where engineers conceptualize designs, translate them into code, and iteratively refine the implem...