Latest AI and machine learning research in covid-19 for healthcare professionals.
High-quality computed tomography (CT) scans are essential for accurate diagnostic and therapeutic decisions, but the presence of metal objects within the body can produce distortions that lower image quality. Deep learning (DL) approaches using image-to-image translation for metal artifact reduction (MAR) show promise over traditional methods but often introduce secondary artifacts. Additionally, ...
Song et al. (2024), "Prediction of PFAS bioaccumulation in different plant tissues with machine learning models based on molecular fingerprints," employed machine learning methods, such as XGBoost and SHapley Additive exPlanations (SHAP), to predict PFAS bioaccumulation, reporting high predictive accuracy. However, this commentary critically examines their interpretation of feature importance, sin...
Convolutional neural networks (CNNs) can effectively extract local features, while Vision Transformer excels at capturing global features. Combining t...
Neural models produce promising results when solving Vehicle Routing Problems (VRPs), but may often fall short in generalization. Recent attempts to e...
Extracellular vesicles (EVs), which are secreted by various cell types, hold significant potential for cancer therapy. However, there are several chal...
Accurate lesion tracking in temporal mammograms is essential for monitoring breast cancer progression and facilitating early diagnosis. However, aut...
The rapid proliferation of modified images on social networks that are driven by widely accessible editing tools demands robust forensic tools for d...
Medical image data is less accessible than in other domains due to privacy and regulatory constraints. In addition, labeling requires costly, time-i...
Medical image data is less accessible than in other domains due to privacy and regulatory constraints. In addition, labeling requires costly, time-i...
Diffusion and flow matching models have significantly advanced media generation, yet their design space is well-explored, somewhat limiting further ...
Advancements in generative models have enabled image inpainting models to generate content within specific regions of an image based on provided pro...
Diffusion-based generative models have shown promise in synthesizing histopathology images to address data scarcity caused by privacy constraints. D...
This study aims to integrate cross-disease omics data and perform multidimensional analysis to uncover the molecular basis of schizophrenia (SCZ) and ...
Fever screening based on infrared thermographs (IRTs) is a viable mass screening approach during infectious disease pandemics, such as Ebola and SAR...
Smartphone-based heart rate (HR) monitoring apps using finger-over-camera photoplethysmography (PPG) face significant challenges in performance eval...
Existing feedforward subject-driven video customization methods mainly study single-subject scenarios due to the difficulty of constructing multi-su...
We present our solution for the Multi-Source COVID-19 Detection Challenge, which aims to classify chest CT scans into COVID and Non-COVID categories...
Collecting pixel-level labels for medical datasets can be a laborious and expensive process, and enhancing segmentation performance with a scarcity ...
Text-to-image retrieval (TIR) aims to find relevant images based on a textual query, but existing approaches are primarily based on whole-image capt...
Recent benchmarks reveal that models for single-cell perturbation response are often outperformed by simply predicting the dataset mean. We trace th...