Staffing & Scheduling

Training-Free Entity-Level Few-Shot Segmentation of Remote Sensing Images with Advection Refinement

Existing cross-domain few-shot segmentation approaches suffer from high training costs due to source-domain episodic training and pixel-wise dense prediction, while often producing fragmented and noisy predictions. To overcome these issues, we propose a training-free entity-level few-shot segmentation framework for remote sensing images with advection refinement. Specifically, we first leverage SA...

Mitigating Class-Tail Undercoverage in Medical Vision-Language Models under Clinical Shift

Medical vision-language models (VLMs) can retain high observed marginal coverage after clinical shift while substantially under-covering an individual disease class. The affected class varies with acquisition protocol and backbone geometry, so source prevalence does not reliably reveal the failure. Existing localized and tail-aware conformal methods respectively adapt to test neighborhoods and sou...

MMOOC: A Comprehensive Benchmark for Out-of-Context Evaluation in Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) have achieved strong performance on a wide range of vision-language tasks, but often fail under imperfect or ...

Rethinking EEG-Based Disease Diagnosis: Decoupling Instance Representation Learning from Subject-Level Supervision

EEG-based disease diagnosis requires one prediction per subject, yet common pipelines segment recordings into short instances, inherit the subject lab...

CalTwin: Towards Calibrated, Shift-Robust Medical World Models via Fisher-Information Regularisation

Medical world models aim to learn a latent state of patient or organ physiology and a transition function that forecasts how that state evolves under ...

Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification

Background/Objectives: Dermoscopic skin lesion classifiers often lose accuracy under domain shift across imaging devices, illumination, and capture ar...

Hierarchical Spatio-Temporal Transformer for Coherent Emergency Department Forecasting

Emergency Departments (EDs) are critical access points in healthcare systems, yet they face persistent pressure from unpredictable patient demand, sea...

What it takes to implement AI in Africa: health-system lessons from developing an ML-enabled maternal risk stratification algorithm in Tanzania

Background Machine learning (ML) has growing potential to support early identification of high-risk pregnancies in resource-constrained settings. Howe...

Agentic Artificial Intelligence as a Catalyst for Administrative Modernization: The Beginning of the End for Traditional Fax Workflows in Healthcare

Background: Healthcare has witnessed administrative staffing roles balloon to twice the number of employed clinicians, resulting in $950 billion per y...

Cross-session generalization in automated behavioral tracking of Galleria mellonella larvae: comparison of classical computer vision, deep learning and generative domain adaptation

Background: Automated behavioral tracking is increasingly used in biological and biomedical research; however, robustness across heterogeneous imaging...

OrthKD: Extracting Generalized Clinical Knowledge from Heterogeneous Teachers for Lightweight Deployment

Deploying diabetic retinopathy (DR) screening models in primary care requires edge-efficient systems that remain accurate, safe, and reliable under do...

VetClaw: An Edge-Cloud Multimodal Agentic System for Veterinary Disease Screening

We present VetClaw, an edge-cloud multimodal agentic system for early veterinary disease screening. VetClaw uses a camera module as an edge sensing de...

Analysis of the Shortcut Learning and Clever Hans Effect in CNN based ECG Image Classification

Deep learning models for ECG image classification may achieve high accuracy by exploiting non-physiological visual cues instead of ECG waveform morpho...

The Lift Spectrum: How Measurement-to-Space Adaptivity Shapes Robustness in Image-Free Single-Pixel Sensing

Single-pixel sensing encodes a scene as a short sequence of coded measurements, and image-free methods infer the task directly from that sequence. Rem...

Optimal Transport Image Representation and Deep Covariance Alignment (CORAL) for Control Valve Stiction Detection

Control valve stiction is a common cause of unwanted oscillations and poor control-loop performance in industrial processes. Data-driven methods can a...

Decoupling Cross-Modality Manifold Discrepancy: Leveraging Visible Diffusion Priors for Infrared Super-Resolution

Infrared image super-resolution (IISR) mitigates the limitations imposed by low spatial resolution. Existing methods have recognized that IISR should ...

Muon Reduces the Training Cost of Regulatory DNA Transformers

Gene-therapy design depends on identifying regulatory sequences that drive the right level, timing, and cell-type specificity of expression. Regulator...

Domain Shift in Echocardiography: Interpretable Quantification and Prediction of Cross-Dataset Left Ventricular Segmentation

Cross-dataset generalisation remains a major barrier to clinical deployment of echocardiographic left ventricular segmentation, yet the sources of thi...

Development and Validation of Machine Learning Models for Predicting 13 or More Sections in Mohs Micrographic Surgery

Background: Cases requiring 13 or more tissue sections in Mohs micrographic surgery (MMS) demand extended operative time, additional resources, and of...

Multi-model Segmentation and Morphometric Quantification of Cerebral Amyloid Angiopathy in Alzheimer's Disease Whole Slide Histopathology Images

Introduction: Cerebral amyloid angiopathy (CAA) is characterized by amyloid-beta deposition in cortical and leptomeningeal vessels and associated with...