Unsupervised Discovery of 3D Hierarchical Structure with Generative Diffusion Features

• URL: http://arxiv.org/abs/2305.00067v2
• Date: Tue, 10 Oct 2023 04:01:38 GMT
• Title: Unsupervised Discovery of 3D Hierarchical Structure with Generative Diffusion Features
• Authors: Nurislam Tursynbek, Marc Niethammer
• Abstract summary: We show that features of diffusion models capture different hierarchy levels in 3D biomedical images. We train a predictive unsupervised segmentation network that encourages the decomposition of 3D volumes into meaningful nested subvolumes. Our models achieve better performance than prior unsupervised structure discovery approaches on challenging synthetic datasets and on a real-world brain tumor MRI dataset.
• Score: 22.657405088126012
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Inspired by recent findings that generative diffusion models learn semantically meaningful representations, we use them to discover the intrinsic hierarchical structure in biomedical 3D images using unsupervised segmentation. We show that features of diffusion models from different stages of a U-Net-based ladder-like architecture capture different hierarchy levels in 3D biomedical images. We design three losses to train a predictive unsupervised segmentation network that encourages the decomposition of 3D volumes into meaningful nested subvolumes that represent a hierarchy. First, we pretrain 3D diffusion models and use the consistency of their features across subvolumes. Second, we use the visual consistency between subvolumes. Third, we use the invariance to photometric augmentations as a regularizer. Our models achieve better performance than prior unsupervised structure discovery approaches on challenging biologically-inspired synthetic datasets and on a real-world brain tumor MRI dataset.

Related papers

• Large Spatial Model: End-to-end Unposed Images to Semantic 3D [79.94479633598102]
  Large Spatial Model (LSM) processes unposed RGB images directly into semantic radiance fields. LSM simultaneously estimates geometry, appearance, and semantics in a single feed-forward operation. It can generate versatile label maps by interacting with language at novel viewpoints.
  arXiv  Detail & Related papers  (2024-10-24T17:54:42Z)
• GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image [94.56927147492738]
  We introduce GeoWizard, a new generative foundation model designed for estimating geometric attributes from single images. We show that leveraging diffusion priors can markedly improve generalization, detail preservation, and efficiency in resource usage. We propose a simple yet effective strategy to segregate the complex data distribution of various scenes into distinct sub-distributions.
  arXiv  Detail & Related papers  (2024-03-18T17:50:41Z)
• Large Generative Model Assisted 3D Semantic Communication [51.17527319441436]
  We propose a Generative AI Model assisted 3D SC (GAM-3DSC) system. First, we introduce a 3D Semantic Extractor (3DSE) to extract key semantics from a 3D scenario based on user requirements. We then present an Adaptive Semantic Compression Model (ASCM) for encoding these multi-perspective images. Finally, we design a conditional Generative adversarial network and Diffusion model aided-Channel Estimation (GDCE) to estimate and refine the Channel State Information (CSI) of physical channels.
  arXiv  Detail & Related papers  (2024-03-09T03:33:07Z)
• A Generative Machine Learning Model for Material Microstructure 3D Reconstruction and Performance Evaluation [4.169915659794567]
  The dimensional extension from 2D to 3D is viewed as a highly challenging inverse problem from the current technological perspective. A novel generative model that integrates the multiscale properties of U-net with and the generative capabilities of GAN has been proposed. The model's accuracy is further improved by combining the image regularization loss with the Wasserstein distance loss.
  arXiv  Detail & Related papers  (2024-02-24T13:42:34Z)
• Self-Supervised Representation Learning for Nerve Fiber Distribution Patterns in 3D-PLI [36.136619420474766]
  3D-PLI is a microscopic imaging technique that enables insights into the fine-grained organization of myelinated nerve fibers with high resolution. Best practices for observer-independent characterization of fiber architecture in 3D-PLI are not yet available. We propose the application of a fully data-driven approach to characterize nerve fiber architecture in 3D-PLI images using self-supervised representation learning.
  arXiv  Detail & Related papers  (2024-01-30T17:49:53Z)
• StableDreamer: Taming Noisy Score Distillation Sampling for Text-to-3D [88.66678730537777]
  We present StableDreamer, a methodology incorporating three advances. First, we formalize the equivalence of the SDS generative prior and a simple supervised L2 reconstruction loss. Second, our analysis shows that while image-space diffusion contributes to geometric precision, latent-space diffusion is crucial for vivid color rendition.
  arXiv  Detail & Related papers  (2023-12-02T02:27:58Z)
• Lymphoma segmentation from 3D PET-CT images using a deep evidential network [20.65641432056608]
  An automatic evidential segmentation method is proposed to segment lymphomas from 3D Positron Emission Tomography (PET) and Computed Tomography (CT) images. The architecture is composed of a deep feature-extraction module and an evidential layer. The proposed combination of deep feature extraction and evidential segmentation is shown to outperform the baseline UNet model.
  arXiv  Detail & Related papers  (2022-01-31T09:34:38Z)
• Hierarchical Graph Networks for 3D Human Pose Estimation [50.600944798627786]
  Recent 2D-to-3D human pose estimation works tend to utilize the graph structure formed by the topology of the human skeleton. We argue that this skeletal topology is too sparse to reflect the body structure and suffer from serious 2D-to-3D ambiguity problem. We propose a novel graph convolution network architecture, Hierarchical Graph Networks, to overcome these weaknesses.
  arXiv  Detail & Related papers  (2021-11-23T15:09:03Z)
• Learning Hyperbolic Representations for Unsupervised 3D Segmentation [3.516233423854171]
  We propose learning effective representations of 3D patches for unsupervised segmentation through a variational autoencoder (VAE) with a hyperbolic latent space and a proposed gyroplane convolutional layer. We demonstrate the effectiveness of our hyperbolic representations for unsupervised 3D segmentation on a hierarchical toy dataset, BraTS whole tumor dataset, and cryogenic electron microscopy data.
  arXiv  Detail & Related papers  (2020-12-03T02:15:31Z)
• Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN [52.851990439671475]
  We propose a novel end-to-end GAN architecture that can generate high-resolution 3D images. We achieve this goal by using different configurations between training and inference. Experiments on 3D thorax CT and brain MRI demonstrate that our approach outperforms state of the art in image generation.
  arXiv  Detail & Related papers  (2020-08-05T02:33:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.