Hence to make dynamic for the image segmentation purpose, the existing deep learning-based frameworks for medical image segmentation have been updated by integrating advanced architectures to observe ...

The AttendSeg deep learning model performs semantic segmentation at an accuracy that is almost on-par with RefineNet while cutting down the number of parameters to 1.19 million.

This is the official code of AIDE, a deep learning framework for automatic medical image segmentation with imperfect datasets, including those having limited annotations, lacking target domain ...

Tongue image segmentation is important in tongue diagnosis, oral disease diagnosis, and health monitoring in traditional Chinese medicine (TCM). Traditional image segmentation methods require a large ...

Deep CNN based pixel-wise semantic segmentation model with >80% mIOU (mean Intersection Over Union). Trained on cityscapes dataset, which can be effectively implemented in self driving vehicle systems ...

Image semantic segmentation is ubiquitously used in scene understanding applications, such as AI Camera, which require high accuracy and efficiency. Deep learning has significantly advanced the ...

By utilizing these three classic deep learning models, we can fully validate the feasibility of deep learning methods for RA segmentation task. Preprocessed images obtained by methods described in ...