Traitement d’images avec Deep Learning
Appliquez le Deep Learning à des applications de traitement d'images en utilisant Deep Learning Toolbox™ avec Image Processing Toolbox™ et Medical Imaging Toolbox™.
|Medical Image Labeler
|Interactively explore, label, and publish animations of 2-D or 3-D medical image data (depuis R2022b)
- Preprocess Data for Domain-Specific Deep Learning Applications
Perform deterministic or randomized data processing for domains such as image processing, object detection, semantic segmentation, signal and audio processing, and text analytics.
- Augment Images for Deep Learning Workflows
This example shows how you can perform common kinds of randomized image augmentation such as geometric transformations, cropping, and adding noise.
- Preprocess Images for Deep Learning
Learn how to resize images for training, prediction, and classification, and how to preprocess images using data augmentation, transformations, and specialized datastores.
- Preprocess Volumes for Deep Learning
Read and preprocess volumetric image and label data for 3-D deep learning.
- Preprocess Multiresolution Images for Training Classification Network (Image Processing Toolbox)
This example shows how to prepare datastores that read and preprocess multiresolution whole slide images (WSIs) that might not fit in memory.
- Get Started with GANs for Image-to-Image Translation (Image Processing Toolbox)
Transfer styles and characteristics from one set of images to the scene content of other images by using generative adversarial networks (GANs).
- Create Datastores for Medical Image Semantic Segmentation (Medical Imaging Toolbox)
Create datastores that contain images and pixel label data from a
groundTruthMedicalobject for training semantic segmentation deep learning networks.