Titlebook: Deep Generative Models; Third MICCAI Worksho Anirban Mukhopadhyay,Ilkay Oksuz,Yixuan Yuan Conference proceedings 2024 The Editor(s) (if app

Generosity

惊惶

Towards Generalised Neural Implicit Representations for Image Registrationese representations are image-pair-specific, meaning that for each signal a new multi-layer perceptron has to be trained. In this work, we investigate for the first time the potential of existent NIR generalisation methods for image registration and propose novel methods for the registration of a gr

definition

neologism

Explicate

Anomaly Guided Generalizable Super-Resolution of Chest X-Ray Images Using Multi-level Information Re useful in improving the resolution of medical images including chest x-rays. Medical images with superior resolution may subsequently lead to an improved diagnosis. However, SISR methods for medical images are relatively rare. We propose a SISR method for chest x-ray images. Our method uses multi-l

grounded

Importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass Segmees, while the applications were mainly limited to 2D networks without exploiting potential benefits from the 3D formulation. In this work, we studied the DDPM-based segmentation model for 3D multiclass segmentation on two large multiclass data sets (prostate MR and abdominal CT). We observed that th

愚蠢人

小隔间

Unsupervised Anomaly Detection in 3D Brain FDG PET: A Benchmark of 17 VAE-Based Approachesedical imaging. Among all the existing models, the variational autoencoder (VAE) has proven to be efficient while remaining simple to use. Much research to improve the original method has been achieved in the computer vision literature, but rarely translated to medical imaging applications. To fill

CRASS

Incorporate

A 3D Generative Model of Pathological Multi-modal MR Images and Segmentations when delivering accurate deep learning models for healthcare applications. In recent years, there has been an increased interest in using these models for data augmentation and synthetic data sharing, using architectures such as generative adversarial networks (GANs) or diffusion models (DMs). None