A new study presents a machine learning model that accurately predicts the compressive strength of high-strength concrete, ...

The symmetry of 3D PEH has been analyzed in statistical and experimental ways. Based on the proposed deep prediction network and efficient 3D mapping strategy (DPEM), we construct an efficient RDH ...

Furthermore, Transformer-based architectures typically require large-scale training datasets to prevent overfitting. When applied to small-sample datasets, such as those often encountered in tongue ...

PACO: Parts and Attributes of Common Objects PACO is a detection dataset that goes beyond traditional object boxes and masks and provides richer annotations such as part masks and attributes. It spans ...

Sports Illustrated has bold predictions for Jahmyr Gibbs and Penei Sewell.

Deep learning–based prediction of neoadjuvant therapy response in HER2-positive breast cancer through histopathology images of core biopsies: A multicenter study.. If you have the appropriate software ...

AI-driven biomarker prediction in oncology: Enhancing pathological image analysis with EXAONEPath.. If you have the appropriate software installed, you can download article citation data to the ...

OORT’s image data set gained front-page visibility on Kaggle, underscoring the momentum behind decentralized and crowd-sourced AI training data solutions.

In this work, we introduce RGBChem, a novel approach for converting chemical compounds into image representations, which are subsequently used to train a convolutional neural network (CNN) to predict ...

We focus on those tasks which learns to map concealed targets in the input image to complex output structures, called Concealed Dense Prediction. Our Contribution.