Latest AI and machine learning research in covid-19 for healthcare professionals.
Segmentation models such as Segment Anything Model (SAM) and SAM2 achieve strong prompt-driven zero-shot performance. However, their training on natural images limits domain transfer to medical data. Consequently, accurate segmentation typically requires extensive fine-tuning and expert-designed prompts. We propose DiffuSAM, a diffusion-based adaptation of SAM2 for prompt-free medical image segmen...
Single image dehazing is often constrained by a trade-off between restoration quality and computational efficiency. While efficient, CNN networks struggle to learn robust priors for dense and non-homogeneous haze. Conversely, diffusion models provide strong generative priors but suffer from severe inference latency and sampling instability. To address these limitations, we propose ZID-Net, a novel...
Large diffusion transformers (DiTs) follow global editing instructions well but consistently leak local edits into unrelated regions, because joint-at...
Background: Radiographic detection of caries lesions adjacent to restorations is challenging due to limitations of two-dimensional imaging and difficu...
Objective: To propose and retrospectively validate an integrated framework addressing three barriers to clinical translation of readmission prediction...
Precise epitope recognition underpins the efficacy and safety of therapeutic antibodies, yet existing approaches to epitope similarity scoring rely la...
Entrenchment - epistasis that locks in amino acid differences between homologous proteins, so each disfavors substitutions toward the other's state - ...
Despite significant progress in Multi-modal Large Language Models (MLLMs), their clinical reasoning capacity for multi-modal diagnosis remains largely...
Panoramic radiography is a fundamental diagnostic tool in dentistry, offering a comprehensive view of the entire dentition with minimal radiation expo...
Conformational flexibility is fundamental to the function of many proteins and in the case of antibodies can impact key properties such as affinity an...
Predicting the clinical significance of genetic variants remains a central challenge in genomic medicine, with most observed variants classified as va...
Preclinical antibody discovery relies on progressive screening and down-selection of candidate antibodies from large immune repertoires, yet this crit...
Ambient Lighting Normalization (ALN) aims to restore images degraded by complex, spatially varying illumination conditions. Existing methods, such as ...
Instance-level object segmentation across disparate egocentric and exocentric views is a fundamental challenge in visual understanding, critical for a...
Glitches frequently contaminate data in gravitational-wave detectors, complicating the observation and analysis of astrophysical signals. This work in...
We address the challenge of synthetic-to-real transfer in forestry perception where real data have only coarse Tree labels while synthetic data provid...
In this study, we investigate gender bias in a Retrieval-Augmented Generation (RAG) based AI assistant developed for Finnish wellbeing services counti...
Style transfer aims to render a content image with the visual characteristics of a reference style while preserving its underlying semantic layout and...
Real-world industrial inspection requires not only localizing defects, but also explaining them in natural language and generating controlled defect e...
Computational phantoms are widely used in medical imaging research, yet current systems to generate controlled, clinically meaningful anatomical varia...