Titlebook: Deep Generative Models; Second MICCAI Worksh Anirban Mukhopadhyay,Ilkay Oksuz,Yixuan Yuan Conference proceedings 2022 The Editor(s) (if app

一再遛

intuition

Springer Tracts in Mechanical Engineeringge from multiple images for the surgery scene. We conduct experiments using an original dataset of three different types of surgeries. Our experiments show that we can successfully synthesize novel views from the images recorded by the multiple cameras embedded in the surgical lamp.

靦腆

SOW

What is Healthy? Generative Counterfactual Diffusion for Lesion Localizationthy data in DPMs. We improve on previous counterfactual DPMs by manipulating the generation process with implicit guidance along with attention conditioning instead of using classifiers (Code is available at .).

我不死扛

Learning Generative Factors of?EEG Data with?Variational Auto-Encodersamework to learn disease-related mechanisms consistent with current domain knowledge. We also compare the proposed framework with several benchmark approaches and indicate its classification performance and interpretability advantages.

出生

An Image Feature Mapping Model for?Continuous Longitudinal Data Completion and?Generation of?Synthetg progression in longitudinal data. Furthermore, we applied the proposed model on a complex neuroimaging database extracted from ADNI. All datasets show that the model is able to learn overall (disease) progression over time.

Biguanides

Novel View Synthesis for?Surgical Recordingge from multiple images for the surgery scene. We conduct experiments using an original dataset of three different types of surgeries. Our experiments show that we can successfully synthesize novel views from the images recorded by the multiple cameras embedded in the surgical lamp.

FLUSH

Anomaly Detection Using Generative Models and?Sum-Product Networks in?Mammography Scansith Random and Tensorized Sum-Product Networks on mammography images using patch-wise processing and observe superior performance over utilizing the models standalone and state-of-the-art in anomaly detection for medical data.

palliate

Gabriela Goldschmidt,William L. Porterr than several baseline methods including direct application of state of the art nuclei segmentation methods such as Cellpose and HoVer-Net, trained on H &E and a generative method, DeepLIIF, using two public IHC image datasets.

外露

Gilbert D. Logan,David F. Radcliffees 3D medical images. The model can easily be conditioned on meta data, for example available patient information. We evaluate the quality of the generated images and compare our model against the 3D-StyleGAN model which is also designed for 3D medical image synthesis.