Stochastic Segmentation with Conditional Categorical Diffusion Models

• URL: http://arxiv.org/abs/2303.08888v5
• Date: Mon, 11 Sep 2023 08:27:13 GMT
• Title: Stochastic Segmentation with Conditional Categorical Diffusion Models
• Authors: Lukas Zbinden, Lars Doorenbos, Theodoros Pissas, Adrian Thomas Huber, Raphael Sznitman, Pablo M\'arquez-Neila
• Abstract summary: We propose a conditional categorical diffusion model (CCDM) for semantic segmentation based on Denoising Diffusion Probabilistic Models. Our results show that CCDM achieves state-of-the-art performance on LIDC, and outperforms established baselines on the classical segmentation dataset Cityscapes.
• Score: 3.8168879948759953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic segmentation has made significant progress in recent years thanks to deep neural networks, but the common objective of generating a single segmentation output that accurately matches the image's content may not be suitable for safety-critical domains such as medical diagnostics and autonomous driving. Instead, multiple possible correct segmentation maps may be required to reflect the true distribution of annotation maps. In this context, stochastic semantic segmentation methods must learn to predict conditional distributions of labels given the image, but this is challenging due to the typically multimodal distributions, high-dimensional output spaces, and limited annotation data. To address these challenges, we propose a conditional categorical diffusion model (CCDM) for semantic segmentation based on Denoising Diffusion Probabilistic Models. Our model is conditioned to the input image, enabling it to generate multiple segmentation label maps that account for the aleatoric uncertainty arising from divergent ground truth annotations. Our experimental results show that CCDM achieves state-of-the-art performance on LIDC, a stochastic semantic segmentation dataset, and outperforms established baselines on the classical segmentation dataset Cityscapes.

Related papers

• FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transforms [60.195642571004804]
  We propose FlowSDF, an image-guided conditional flow matching framework to represent the signed distance function (SDF) By learning a vector field that is directly related to the probability path of a conditional distribution of SDFs, we can accurately sample from the distribution of segmentation masks.
  arXiv  Detail & Related papers  (2024-05-28T11:47:12Z)
• SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation [69.42764583465508]
  We explore the potential of generative image diffusion to address the scarcity of annotated data in earth observation tasks. To the best of our knowledge, we are the first to generate both images and corresponding masks for satellite segmentation.
  arXiv  Detail & Related papers  (2024-03-25T10:30:22Z)
• EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models [52.3015009878545]
  We develop an image segmentor capable of generating fine-grained segmentation maps without any additional training. Our framework identifies semantic correspondences between image pixels and spatial locations of low-dimensional feature maps. In extensive experiments, the produced segmentation maps are demonstrated to be well delineated and capture detailed parts of the images.
  arXiv  Detail & Related papers  (2024-01-22T07:34:06Z)
• Towards Better Certified Segmentation via Diffusion Models [62.21617614504225]
  segmentation models can be vulnerable to adversarial perturbations, which hinders their use in critical-decision systems like healthcare or autonomous driving. Recently, randomized smoothing has been proposed to certify segmentation predictions by adding Gaussian noise to the input to obtain theoretical guarantees. In this paper, we address the problem of certifying segmentation prediction using a combination of randomized smoothing and diffusion models.
  arXiv  Detail & Related papers  (2023-06-16T16:30:39Z)
• Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation [18.956306942099097]
  Conditional diffusion models (CDM) is capable of generating images subject to specific distributions. We utilize category-aware semantic information underlied in CDM to get the prediction mask of the target object. Our method outperforms state-of-the-art CAM and diffusion model methods on two public medical image segmentation datasets.
  arXiv  Detail & Related papers  (2023-06-06T17:29:26Z)
• Score-Based Generative Models for Medical Image Segmentation using Signed Distance Functions [11.137438870686026]
  We propose a conditional score-based generative modeling framework to represent the signed distance function (SDF) The advantage of leveraging the SDF is a more natural distortion when compared to that of binary masks.
  arXiv  Detail & Related papers  (2023-03-10T14:55:35Z)
• Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation [58.17907376475596]
  We investigate normal-to-adverse condition model adaptation for semantic segmentation. Our method -- CMA -- leverages such image pairs to learn condition-invariant features via contrastive learning. We achieve state-of-the-art semantic segmentation performance for model adaptation on several normal-to-adverse adaptation benchmarks.
  arXiv  Detail & Related papers  (2023-03-09T11:48:29Z)
• SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation [58.61946589036262]
  This paper studies a practical domain adaptive (DA) semantic segmentation problem where only pseudo-labeled target data is accessible through a black-box model. Due to the domain gap and label shift between two domains, pseudo-labeled target data contains mixed closed-set and open-set label noises. We propose a simplex noise transition matrix (SimT) to model the mixed noise distributions in DA semantic segmentation and formulate the problem as estimation of SimT.
  arXiv  Detail & Related papers  (2022-03-29T02:48:08Z)
• Diffusion Models for Implicit Image Segmentation Ensembles [1.444701913511243]
  We present a novel semantic segmentation method based on diffusion models. By modifying the training and sampling scheme, we show that diffusion models can perform lesion segmentation of medical images. Compared to state-of-the-art segmentation models, our approach yields good segmentation results and, additionally, meaningful uncertainty maps.
  arXiv  Detail & Related papers  (2021-12-06T16:28:15Z)

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.