Medical image segmentation is one of the most important tasks in modern healthcare. Every pixel in a scan tells a story, whether it marks a healthy cell, a cancerous growth, or a vital organ boundary.

Over the past decade, advancements in machine learning (ML) and deep learning (DL) have revolutionized segmentation accuracy.

Training deep learning models is costly and hard, but not as much as deploying and running them in production. Deci wants to help address that.

Artificial Intelligence (AI) has rapidly emerged as a transformative technology in healthcare, particularly in medical image processing. Medical imaging ...

Z eiss Medical Technology has received CE mark approval for its CIRRUS PathFinder, a clinical support tool that utilises AI ...

Examples of Deep Learning: Image Recognition: Deep learning is used in image recognition systems, like facial recognition or object detection in self-driving cars, where convolutional neural ...

Conceived an international research group, the proposed model uses the convolutional neural network (CNN) architecture U-Net for image segmentation and the the CNN architecture InceptionV3-Net for ...