Titlebook: Deep Generative Models, and Data Augmentation, Labelling, and Imperfections; First Workshop, DGM4 Sandy Engelhardt,Ilkay Oksuz,Yuan Xue Con

拋物線

nutrients

Conditional Generation of Medical Images via Disentangled Adversarial Inferencee variables. We conduct extensive qualitative and quantitative assessments on two publicly available medical imaging datasets (LIDC and HAM10000) and test for conditional image generation and style-content disentanglement. We also show that our proposed model (DRAI) achieves the best disentanglement score and has the best overall performance.

灌溉

怒目而視

crescendo

One-Shot Learning for Landmarks Detectionthm in order to perform automatic organ localization and landmark matching. We investigated both qualitatively and quantitatively the performance of the proposed approach on clinical temporal bone CT volumes. The results show that our one-shot learning scheme converges well and leads to a good accuracy of the landmark positions.

reperfusion

Arrhythmia

Conception of Design Science and its Methods latent space to generate images from a broader domain than what was observed. We show that using our generative approach for ultrasound data augmentation and domain adaptation during training improves the performance of the resulting deep learning models, even when tested within the observed domain.

CHOIR

Helena M. Müller,Melanie Reuter-Oppermanndel is trained to generate fake brain connectivity matrices, which are expected to reflect the latent distribution and topological features of the real brain network data. Numerical results show that the BrainNetGAN outperforms the benchmarking algorithms in augmenting the brain networks for AD classification tasks.

Ingenuity

Allergic