澳门沙金在线平台(App Store-VIP认证)-Branding Company

Introduction

Artificial intelligence has made significant breakthroughs in medical image processing, including the intelligent recognition, automatic segmentation, three-dimensional reconstruction, and quantification of lesions in CT, MRI, and ultrasound images, as well as the subsequent intelligent diagnosis and prognosis assessment. This laboratory covers the typical applications of artificial intelligence in medical imaging, including CT, MRI, and ultrasound images, and provides detailed processes of medical image processing and deep learning model training, including data annotation, model training, model call and deployment, to help students quickly get started and master relevant knowledge and skills.

Corporate Positions: Data Collection and Annotation Engineer, AI Training Engineer, AI Application Development Engineer

Applicable Majors: AI Engineering Technology/Medical Imaging Technology/Computer-related majors

Course Products: CT Imaging Principles Training, Ultrasound Imaging Principles Training, Medical Image Enhancement Training, Image Segmentation Training, Image Registration Training, CT Simulation Operation Training

Project Products: Head CT Image Motion Artifact Recognition, Automatic Left Ventricle Segmentation in Echocardiography, Head CT Image Motion Artifact Recognition Based on Neural Network Architecture Search (NAS), Generating Brain CT Simulation Images Using DCGAN, Automatic Segmentation of COVID-19 CT Images Using AI, Brain Tumor MRI Image Segmentation Using nnU-Net Application scenarios: Professional teaching, comprehensive training, competition training

State-of-the-art and focused

Utilizing cutting-edge artificial intelligence algorithms to solve hot issues in medical imaging

Technology coverage across the board

The project covers the entire technology chain of medical imaging processing, including core technology areas such as imaging, enhancement, segmentation, and registration.

Self-developed imaging algorithm

The CT imaging process in the simulation system is visually visible