A5033530987- AI in cancer detection
- Generative Adversarial Networks and Image Synthesis
- Advanced Image Processing Techniques
- Law in Society and Culture
- Face recognition and analysis
Universitat de Barcelona
2021
We consider a set of arithmetic operations in the latent space Generative Adversarial Networks (GANs) to edit histopathological images. analyze thousands image patches from whole-slide images breast cancer metastases histological lymph node sections. Image files were downloaded pathology contests CAMELYON 16 and 17. show that widely known architectures, such as: Deep Convolutional (DCGAN) Conditional (cDCGAN), allow editing using semantic concepts represent underlying visual patterns images,...
Generative adversarial networks (GANs) provide powerful architectures for deep generative learning. GANs have enabled us to achieve an unprecedented degree of realism in the creation synthetic images human faces, landscapes, and buildings, among others. Not only image generation, but also manipulation is possible with GANs. learning models are inherently limited their creative abilities because a focus on perfection. We investigated potential GAN’s latent spaces encode expressions,...