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.

Segmentation accuracy has improved through the use of deep learning-based models compared with traditional methods for dividing heart images (Geert et al., 2017). However, the models still lack the ...

Artificial Intelligence and deep learning models have evolved rapidly in the last decade and successfully applied to face recognition, autonomous driving, satellite imaging, robotics, and many more.

The codes in this repository are based on our work presented in the paper Attention Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images. An important avenue for improved ...

A module to train cell segmentation models using deep learning, as outlined in "Cell segmentation using deep learning: comparing label and label-free approaches using hyper-labeled image stacks" (WD ...

Data pre-processing. To better adapt to the training stage of the deep learning network, we pre-process the CT images in our dataset. Specifically, the window level (WL)/window width (WW) of each CT ...

This paper presents a comprehensive review of the principle and application of deep learning in retinal image analysis. Many eye diseases often lead to blindness in the absence of proper clinical ...

A new technical paper titled “A Universal AI-Powered Segmentation Model for PCBA and Semiconductor” was published by researchers at Nordson Corporation. “This paper introduces a novel universal deep ...