Latest AI and machine learning research in covid-19 for healthcare professionals.
Medical visual question answering (Med-VQA) aims to answer clinically relevant questions grounded in medical images. However, existing multimodal large language models (MLLMs) often exhibit shortcut answering, producing plausible responses by exploiting language priors or dataset biases while insufficiently attending to visual evidence. This behavior undermines clinical reliability, especially whe...
Background: Formalin-fixed paraffin-embedding (FFPE) is a widely used, cost-effective method for long-term storage of clinical samples. However, fixation is known to introduce damage to nucleic acids that can present as artifactual bases in sequencing otherwise absent from higher fidelity storage methods such as fresh freezing (FF). Various machine learning methods exist for filtering these varian...
We propose a deep learning framework for COVID-19 detection and disease classification from chest CT scans that integrates both 2.5D and 3D representa...
Predicting immunoglobulin-antigen (Ig-Ag) binding remains a significant challenge due to the paucity of experimentally-resolved complexes and the limi...
Robust detection of COVID-19 from chest CT remains challenging in multi-institutional settings due to substantial source shift, source imbalance, and ...
Recent real-time detection transformers have gained popularity due to their simplicity and efficiency. However, these detectors do not explicitly mode...
The COVID-19 pandemic exposed critical limitations in diagnostic workflows: RT-PCR tests suffer from slow turnaround times and high false-negative rat...
Vision-language models (VLMs) have advanced rapidly, yet they still struggle with basic spatial reasoning. Despite strong performance on general bench...
This paper presents a new ambient light normalization framework, DINOLight, that integrates the self-supervised model DINOv2's image understanding cap...
Percentage Brain Volume Change (PBVC) derived from Magnetic Resonance Imaging (MRI) is a widely used biomarker of brain atrophy, with SIENA among the ...
Background. Atherosclerosis is increasingly recognized as a chronic immunometabolic disorder involving complex interactions between circulating immune...
Targets supported by human genetic associations are more than twice as likely to progress from clinical development to approval. Genome-wide associati...
We present TornadoNet, a comprehensive benchmark for automated street-level building damage assessment evaluating how modern real-time object detectio...
Background: Assessment of the prostatic neurovascular bundles on MRI is clinically relevant for staging and treatment planning but remains technically...
Noncoding genetic variation contributes to brain disorder risk, but the mechanisms through which it acts in specific brain cell types remain unclear. ...
We propose \textbf{A$^2$-Edit}, a unified inpainting framework for arbitrary object categories, which allows users to replace any target region with a...
Large pretrained diffusion models have significantly enhanced the quality of generated videos, and yet their use in real-time streaming remains limite...
While Diffusion Models excel in text-to-image synthesis, they often suffer from concept omission when synthesizing complex multi-instance scenes. Exis...
ABSTRACT Background: Sezary syndrome (SS) represents an aggressive leukemic variant of cutaneous T-cell lymphoma (CTCL) with distinct clinical behavio...
Infertility generates profound psychological and social distress for both women and men, yet mens communicative experiences remain comparatively under...