Medical Ethics / Professional Responsibility

Hedge Scope Detection in Biomedical Texts: An Effective Dependency-Based Method.

Hedge detection is used to distinguish uncertain information from facts, which is of essential importance in biomedical information extraction. The task of hedge detection is often divided into two subtasks: detecting uncertain cues and their linguistic scope. Hedge scope is a sequence of tokens including the hedge cue in a sentence. Previous hedge scope detection methods usually take all tokens i...

Near-Bayesian Support Vector Machines for imbalanced data classification with equal or unequal misclassification costs.

Support Vector Machines (SVMs) form a family of popular classifier algorithms originally developed to solve two-class classification problems. However, SVMs are likely to perform poorly in situations with data imbalance between the classes, particularly when the target class is under-represented. This paper proposes a Near-Bayesian Support Vector Machine (NBSVM) for such imbalanced classification ...

Fast Computation of Hemodynamic Sensitivity to Lumen Segmentation Uncertainty.

Patient-specific blood flow modeling combining imaging data and computational fluid dynamics can aid in the assessment of coronary artery disease. Acc...

Investigating the use of support vector machine classification on structural brain images of preterm-born teenagers as a biological marker.

Preterm birth has been shown to induce an altered developmental trajectory of brain structure and function. With the aid support vector machine (SVM) ...

Selective invocation of shape priors for deformable segmentation and morphologic classification of prostate cancer tissue microarrays.

Shape based active contours have emerged as a natural solution to overlap resolution. However, most of these shape-based methods are computationally e...

Auditing Patient Privacy in Medical Generative Models: Scalable Memorization Detection with DeepSSIM++

While deep generative models offer new opportunities for medical image synthesis and data sharing, their ability to memorize and reproduce training sa...

ARCOS: Zero-shot Boundary Localization for Corneal Layer Segmentation Across Optical Coherence Tomography Devices

Accurate segmentation of corneal layers in optical coherence tomography (OCT) is essential for quantitative assessment of corneal morphology, includin...

Pushing the (Decision) Boundaries: Dynamically Calibrating Differentially Private Noise to Explainability in Federated Learning

Federated Learning (FL) with Differential Privacy (DP) is increasingly adopted to preserve data confidentiality in distributed machine learning. Howev...

Generating Medical Image Counterfactuals using Causal Explanations

Deep learning models have achieved impressive performance in medical image diagnosis, yet their deployment in clinical settings remains constrained by...

Certified Safety Radii in Forecast-Error Space for Wasserstein Distributionally Robust Small Signal Stability-Constrained AC Optimal Power Flow via Lifted Spectrahedral Containment

Directly robustifying small-signal stability in AC optimal power flow is challenging since the stability boundary in the original uncertainty space is...

RealOOB: A Definition-Consistent Real-World Oriented Occlusion Boundary Benchmark

Occlusion boundaries (OBs) are pixel-level image boundaries corresponding to surface visibility discontinuities caused by occlusion. Through precise b...

FAN-LoRA: A Fourier-Adaptive Nonlinear Low-Rank Adaptor for Medical Foundation Model Domain Adaptation

The advent of vision foundation models, notably the Segment Anything Model (SAM), has catalyzed significant advancements in natural image segmentation...

Unsupervised Anatomical Feature Learning via Diffusion Models: Enhanced Medical Image Segmentation with Denoising Diffusion Probabilistic Models

Acquiring pixel-level annotations for medical image segmentation is a severe bottleneck. Traditional U-Net architectures, while effective, learn local...

Steer the Sampling, Not the Kernel Grid: Geometry-Guided Sampling Operator for Volumetric Segmentation

Accurate 3D segmentation is central to quantitative lesion assessment and anatomy mapping for clinical planning and follow-up. Thin, elongated, and fi...

Image-Conditioned Diffusion Models for Quality Assurance of Organ-at-Risk Segmentations in Radiotherapy

Accurate organ-at-risk segmentation is essential for radiotherapy planning, but reviewing segmentations is time-consuming and subjective. We investiga...

CiUNet: A Hybrid Swin-CNN UNet for Medical Image Segmentation

Medical image segmentation requires high accuracy and robustness, yet practical commercial deployment also demands privacy preservation and computatio...

RS$^3$-Prune: Read-Sparse, Store-Sparse Token Pruning for Video Object Segmentation

We introduce RS$^3$-Prune, a training-free token-pruning recipe that instantiates as a small set of inference time hooks atop existing video object se...

GAP-SAM: A Global Artifact Prior for Generalizable AI-Generated Image Manipulation Localization

AI-generated image manipulation localization identifies edited pixels, but its OOD performance lags behind image-level detection partly because pixel ...

Dorsal Hand Images for Immersive (XR) and Privacy-preserving Age Assurance and Child Safety

Ensuring that Extended Reality (XR) environments are age-appropriate is an important regulatory and safety challenge. However, current age assurance o...

Toward Vision Language Model-based Assessment of Clinical Quality and Usability of LGE-MR Images for Cardiac Ablation Planning

LGE cardiac MRI is widely used for left atrial fibrosis assessment and ablation planning in atrial fibrillation patients as knowledge of fibrotic tiss...