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A Configurable Privacy-Preserving MRI Processing Workflow Using Deep Learning-Based Brain Extraction and Adaptive Anatomical Preservation

Structural Magnetic Resonance Imaging (MRI) is widely used in neuroimaging research and clinical practice, but structural MRI volumes may retain facial and cranial anatomical information that raises privacy concerns. Existing deep learning-based brain extraction methods generally produce a single fixed output, limiting flexibility when different applications require different balances between priv...

Retrieval-Augmented Large Language Models for Clinically Aligned Adverse Event Coding in Acute Myeloid Leukemia Clinical Trials

Background: Adverse event (AE) coding is essential for safety monitoring in oncology clinical trials, particularly in acute myeloid leukemia (AML), where intensive therapies are associated with frequent and heterogeneous toxicities requiring standardized MedDRA (Medical Dictionary for Regulatory Activities) coding. However, manual Low-Level Term (LLT) assignment remains labor-intensive, subjective...

Spatial and Machine Learning Analysis of Breast and Cervical Cancer Screening Uptake in Ghana: Evidence from the 2022 Ghana Demographic and Health Survey

Abstract Background Breast and cervical cancer screening in Ghana remains low, and several analyses of the Ghana Demographic and Health Survey (GDHS) ...

AFilter: Improved Antibody Epitope Prediction by Machine Learning-Optimized Interface Energy Filtering of AlphaFold3-Predicted Complex

AlphaFold3 (AF3) predicts protein-complex structures from sequence with near-experimental accuracy on many targets, substantially lowering the cost of...

Assessing Codon Language Models for Context-Aware Codon Optimization in Nucleic Acid-Based Medicines

Codon optimization uses synonymous sequence changes to improve the expression and therapeutic performance of nucleic acid-based medicines. Masked lang...

Noncoding regulatory mutations contribute to the aberrant gene expression in neuroblastoma.

Comprehensive analyses of whole-genome and exome sequencing data from high-risk neuroblastoma tumors have revealed relatively few recurrent, clinicall...

FIRM: Fine-Grained Intra-Token Representation of Masks for Remote Sensing Reasoning Segmentation

Reasoning segmentation requires multimodal large language models (MLLMs) to translate implicit instructions into precise pixel-level masks. MLLMs enco...

Source-Agnostic Image Translation Based on Latent Aware Adaptive Masking

In this work, we propose a source-agnostic framework that dynamically refines a binary mask throughout the reverse diffusion process by computing the ...

MaternaAI: Enhancing Equitable Maternal Healthcare in Kerala with Fairness-Aware and Explainable Learning Models

Maternal healthcare prediction systems often suffer from algorithmic biases due to socio-economic disparities and imbalanced datasets, limiting their ...

EEG-PRISM: Physiologically-Grounded Interpretability of Predictions by EEG Foundation Models

Objective: Foundation models represent the next advancement in AI for EEG analysis; however current explainable AI techniques provide attribution scor...

PatchGen: Learning Soft Intra-Image Predictive Subsets for Visual Generalization

Visual classifiers are expected to generalize under data shifts, target shifts, and their combinations, yet most existing methods focus on domain inva...

DiCoR: Decoupled Referent Disambiguation and Contour Recalibration for Efficient Referring Remote Sensing Image Segmentation

Referring remote sensing image segmentation (RRSIS) aims to delineate targets specified by natural language expressions in remote sensing imagery. Exi...

Zero-OVCD: Bridging Training-Free Foundation Models and Pseudo-Label Learning for Open-Vocabulary Change Detection

Open-vocabulary change detection (OVCD) enables the identification of user-specified land-cover changes in bitemporal remote sensing images, but exist...

CoDiR: Confidence-Guided Diffusion Refinement for Semi-Supervised Histopathology Segmentation

Semi-supervised histopathology segmentation is challenging due to scarce annotations and unreliable pseudo-labels in ambiguous gland regions. To addre...

DistilVDR: A Compact End-to-End Visual Document Retriever via Dual-Student Distillation

Visual document retrieval (VDR) is dominated by multi-billion-parameter models that are slow to index at full corpus scale and expensive to serve. Pri...

AlbumentationsX: One Augmentation Pipeline for Images and Related Annotations

Augmentation can corrupt a training example when an image and its annotations receive different random changes. A crop must use the same coordinates f...

Detecting Clear Contact Lenses for Iris Recognition: A Two-Stage Mask-Guided Attention Approach

This work focuses on the impact and detection of clear contact lenses in the context of iris recognition. While the detection of cosmetic or patterned...

Contrastive Mask Fidelity: Reference-Free Auditing of Ground-Truth Masks in Remote Sensing Semantic Segmentation

Semantic segmentation models are trained and evaluated against human-drawn masks, yet remote-sensing annotations are often coarse, incomplete, or misa...

MedPixel: A Unified Pixel-Language Model for Medical Reasoning and Segmentation

Reliable medical image understanding requires models to connect clinical language and visual reasoning with pixel-level grounding. Yet medical vision-...

Context-Dependent FHIR Serialisation Strategies for Clinical LLM Deployment: A Multi-Layer Benchmark on UK Core Data

The choice of FHIR-to-text serialisation format significantly impacts clinical LLM quality (Kruskal-Wallis H=163.86, p<10^-33, delta=0.24 on a 5-point...