A study has found that the way medical images are prepared before analysis can have a significant impact on the performance of deep learning models.

The BCTVNet neural network provides accurate and rapid target volume delineation for cervical cancer brachytherapy ...

A research team developed a fully automated, drone-based phenotyping workflow that can measure key peanut canopy and ...

FLAMeS, a new convolutional neural network, enhances MS lesion segmentation accuracy using only T2-weighted FLAIR images, ...

Master’s thesis position (M.Sc. student) in Deep Learning for Healthcare.

A research team has now developed a new few-shot semantic segmentation framework, SegPPD-FS, capable of identifying infected regions from only one or a few labeled samples.

Alongside the model, a high-quality benchmark dataset covering 101 pest and disease classes has been publicly released. Together, they offer a ...

A research team has developed a dual-UAV cooperative color correction system (CoF-CC) that integrates synchronized drone flights with a ColorChecker reference chart for real-time calibration.

In computer vision applications, Cyberway's self-developed high-performance image recognition algorithms have been successfully deployed in visual inventory counting scenarios, enabling automated ...

Forests and plantations play a vital role in carbon sequestration, yet accurately monitoring their growth remains costly and labor-intensive ...

The company has established joint research and development teams with Hunan University, Nanjing University of Posts and Telecommunications, and Soochow University to tackle research challenges. It has ...