A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis

• URL: http://arxiv.org/abs/2111.06398v1
• Date: Wed, 10 Nov 2021 22:48:55 GMT
• Title: A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis
• Authors: Jiarong Ye, Yuan Xue, Peter Liu, Richard Zaino, Keith Cheng, Xiaolei Huang
• Abstract summary: We build upon state-of-the-art generative adversarial networks (GANs) to achieve an attribute-aware image synthesis model, termed AttributeGAN. We conduct experiments on a histopathology dataset containing stained H&E images of urothelial carcinoma.
• Score: 13.353006960284159
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative models have been applied in the medical imaging domain for various image recognition and synthesis tasks. However, a more controllable and interpretable image synthesis model is still lacking yet necessary for important applications such as assisting in medical training. In this work, we leverage the efficient self-attention and contrastive learning modules and build upon state-of-the-art generative adversarial networks (GANs) to achieve an attribute-aware image synthesis model, termed AttributeGAN, which can generate high-quality histopathology images based on multi-attribute inputs. In comparison to existing single-attribute conditional generative models, our proposed model better reflects input attributes and enables smoother interpolation among attribute values. We conduct experiments on a histopathology dataset containing stained H&E images of urothelial carcinoma and demonstrate the effectiveness of our proposed model via comprehensive quantitative and qualitative comparisons with state-of-the-art models as well as different variants of our model. Code is available at https://github.com/karenyyy/MICCAI2021AttributeGAN.

Related papers

• A Simple Approach to Unifying Diffusion-based Conditional Generation [63.389616350290595]
  We introduce a simple, unified framework to handle diverse conditional generation tasks. Our approach enables versatile capabilities via different inference-time sampling schemes. Our model supports additional capabilities like non-spatially aligned and coarse conditioning.
  arXiv  Detail & Related papers  (2024-10-15T09:41:43Z)
• Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis [62.06970466554273]
  We present Meissonic, which non-autoregressive masked image modeling (MIM) text-to-image elevates to a level comparable with state-of-the-art diffusion models like SDXL. We leverage high-quality training data, integrate micro-conditions informed by human preference scores, and employ feature compression layers to further enhance image fidelity and resolution. Our model not only matches but often exceeds the performance of existing models like SDXL in generating high-quality, high-resolution images.
  arXiv  Detail & Related papers  (2024-10-10T17:59:17Z)
• Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization [12.753792457271953]
  We propose an innovative unsupervised augmentation solution that harnesses Generative Adversarial Network (GAN) based models. We created synthetic images to incorporate the semantic variations and augmented the training data with these images. We were able to increase the performance of machine learning models and set a new benchmark amongst non-ensemble based models in skin lesion classification.
  arXiv  Detail & Related papers  (2024-10-07T15:09:50Z)
• Unleashing the Potential of Synthetic Images: A Study on Histopathology Image Classification [0.12499537119440242]
  Histopathology image classification is crucial for the accurate identification and diagnosis of various diseases. We show that synthetic images can effectively augment existing datasets, ultimately improving the performance of the downstream histopathology image classification task.
  arXiv  Detail & Related papers  (2024-09-24T12:02:55Z)
• Multi-Branch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction [42.95604565673447]
  This paper presents a novel approach for learned synergistic reconstruction of medical images using multi-branch generative models. We demonstrate the efficacy of our approach on both Modified National Institute of Standards and Technology (MNIST) and positron emission tomography (PET)/ computed tomography (CT) datasets.
  arXiv  Detail & Related papers  (2024-04-12T18:21:08Z)
• Comparison of No-Reference Image Quality Models via MAP Estimation in Diffusion Latents [99.19391983670569]
  We show that NR-IQA models can be plugged into the maximum a posteriori (MAP) estimation framework for image enhancement. Different NR-IQA models are likely to induce different enhanced images, which are ultimately subject to psychophysical testing. This leads to a new computational method for comparing NR-IQA models within the analysis-by-synthesis framework.
  arXiv  Detail & Related papers  (2024-03-11T03:35:41Z)
• Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Images [0.08192907805418582]
  This work contributes an empirical demonstration of the benefits of integrating the adaptive input-image normalization with the Deep Conversaal GAN and Auxiliary GAN to alleviate the mode collapse problems. Results demonstrate that the DCGAN and the ACGAN with adaptive input-image normalization outperform the DCGAN and ACGAN with un-normalized X-ray images.
  arXiv  Detail & Related papers  (2023-09-21T16:43:29Z)
• StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data [129.92449761766025]
  We propose a novel data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. This approach harnesses the power of generative models, marrying the abilities of ChatGPT and text-to-image generative models. Our research includes comprehensive experiments conducted on various datasets.
  arXiv  Detail & Related papers  (2023-08-20T12:43:52Z)
• Classification of lung cancer subtypes on CT images with synthetic pathological priors [41.75054301525535]
  Cross-scale associations exist in the image patterns between the same case's CT images and its pathological images. We propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on CT images.
  arXiv  Detail & Related papers  (2023-08-09T02:04:05Z)
• Is synthetic data from generative models ready for image recognition? [69.42645602062024]
  We study whether and how synthetic images generated from state-of-the-art text-to-image generation models can be used for image recognition tasks. We showcase the powerfulness and shortcomings of synthetic data from existing generative models, and propose strategies for better applying synthetic data for recognition tasks.
  arXiv  Detail & Related papers  (2022-10-14T06:54:24Z)
• DiVAE: Photorealistic Images Synthesis with Denoising Diffusion Decoder [73.1010640692609]
  We propose a VQ-VAE architecture model with a diffusion decoder (DiVAE) to work as the reconstructing component in image synthesis. Our model achieves state-of-the-art results and generates more photorealistic images specifically.
  arXiv  Detail & Related papers  (2022-06-01T10:39:12Z)

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.