ReStainGAN: Leveraging IHC to IF Stain Domain Translation for in-silico Data Generation

• URL: http://arxiv.org/abs/2403.06545v1
• Date: Mon, 11 Mar 2024 09:45:34 GMT
• Title: ReStainGAN: Leveraging IHC to IF Stain Domain Translation for in-silico Data Generation
• Authors: Dominik Winter, Nicolas Triltsch, Philipp Plewa, Marco Rosati, Thomas Padel, Ross Hill, Markus Schick, Nicolas Brieu
• Abstract summary: In-silico datasets can expand the utility of existing annotations to new domains with different staining patterns in computational pathology. We propose a novel approach for the generation of in-silicochemistry (IHC) images by disentangling morphology specific IHC stains into separate image channels in immunofluorescence (IF) images.
• Score: 0.12564343689544843
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The creation of in-silico datasets can expand the utility of existing annotations to new domains with different staining patterns in computational pathology. As such, it has the potential to significantly lower the cost associated with building large and pixel precise datasets needed to train supervised deep learning models. We propose a novel approach for the generation of in-silico immunohistochemistry (IHC) images by disentangling morphology specific IHC stains into separate image channels in immunofluorescence (IF) images. The proposed approach qualitatively and quantitatively outperforms baseline methods as proven by training nucleus segmentation models on the created in-silico datasets.

Related papers

• Cross-Domain Image Synthesis: Generating H&E from Multiplex Biomarker Imaging [0.0]
  We investigate the use of a multi-level Vector-Quantized Generative Adrial Network (VQGAN) to create high-fidelity virtual H&E stains from mIF images.<n>Our results show that while both architectures produce visually plausible stains, the virtual stains generated by our VQGAN provide a more effective substrate for computer-aided diagnosis.
  arXiv  Detail & Related papers  (2025-08-05T21:19:00Z)
• Direct Dual-Energy CT Material Decomposition using Model-based Denoising Diffusion Model [105.95160543743984]
  We propose a deep learning procedure called Dual-Energy Decomposition Model-based Diffusion (DEcomp-MoD) for quantitative material decomposition.<n>We show that DEcomp-MoD outperform state-of-the-art unsupervised score-based model and supervised deep learning networks.
  arXiv  Detail & Related papers  (2025-07-24T01:00:06Z)
• F-ANcGAN: An Attention-Enhanced Cycle Consistent Generative Adversarial Architecture for Synthetic Image Generation of Nanoparticles [3.124884279860061]
  We introduce F-ANcGAN, an attention-enhanced cycle consistent generative adversarial system that can be trained using a limited number of data samples.<n>Our model uses a Style U-Net generator and a U-Net segmentation network equipped with self-attention to capture structural relationships.
  arXiv  Detail & Related papers  (2025-05-23T17:02:22Z)
• Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model [0.0]
  This study integrates edge detection with generative diffusion models to identify grains, eliminate noise, and connect broken segments in alignment with predicted grain boundaries. The presented model was applied to various metals with average grain sizes down to the nanoscale, producing grain morphologies from low-resolution TEM images with an average accuracy of 97.23%.
  arXiv  Detail & Related papers  (2025-04-21T00:46:28Z)
• Causal Discovery from Data Assisted by Large Language Models [50.193740129296245]
  It is essential to integrate experimental data with prior domain knowledge for knowledge driven discovery. Here we demonstrate this approach by combining high-resolution scanning transmission electron microscopy (STEM) data with insights derived from large language models (LLMs) By fine-tuning ChatGPT on domain-specific literature, we construct adjacency matrices for Directed Acyclic Graphs (DAGs) that map the causal relationships between structural, chemical, and polarization degrees of freedom in Sm-doped BiFeO3 (SmBFO)
  arXiv  Detail & Related papers  (2025-03-18T02:14:49Z)
• Augmentation-Based Deep Learning for Identification of Circulating Tumor Cells [0.18846515534317262]
  CTCs are crucial biomarkers in liquid biopsy, offering a noninvasive tool for cancer patient management. Traditionally, DEPArray-acquired digital images are manually analyzed, making the process time-consuming and prone to variability. We present a Deep Learning-based classification pipeline designed to distinguish CTCs from leukocytes in blood samples.
  arXiv  Detail & Related papers  (2025-03-05T11:39:15Z)
• Segmentation-free integration of nuclei morphology and spatial transcriptomics for retinal images [1.2200074914789645]
  SEFI (SEgmentation-Free Integration) is a novel method for integrating morphological features of cell nuclei with spatial transcriptomics data. We demonstrate SEFI on spatially resolved gene expression profiles of the developing retina, acquired using multiplexed single molecule Fluorescence In Situ Hybridization (smFISH)
  arXiv  Detail & Related papers  (2025-02-08T14:03:02Z)
• Exploring structure diversity in atomic resolution microscopy with graph neural networks [18.903519247639355]
  Deep learning is a powerful tool to explore structure diversity in a fast, accurate, and intelligent manner. This work provides a powerful tool to explore structure diversity in a fast, accurate, and intelligent manner.
  arXiv  Detail & Related papers  (2024-10-23T07:48:35Z)
• Dataset Distillation for Histopathology Image Classification [46.04496989951066]
  We introduce a novel dataset distillation algorithm tailored for histopathology image datasets (Histo-DD) We conduct a comprehensive evaluation of the effectiveness of the proposed algorithm and the generated histopathology samples in both patch-level and slide-level classification tasks.
  arXiv  Detail & Related papers  (2024-08-19T05:53:38Z)
• Data-Efficient Molecular Generation with Hierarchical Textual Inversion [48.816943690420224]
  We introduce Hierarchical textual Inversion for Molecular generation (HI-Mol), a novel data-efficient molecular generation method. HI-Mol is inspired by the importance of hierarchical information, e.g., both coarse- and fine-grained features, in understanding the molecule distribution. Compared to the conventional textual inversion method in the image domain using a single-level token embedding, our multi-level token embeddings allow the model to effectively learn the underlying low-shot molecule distribution.
  arXiv  Detail & Related papers  (2024-05-05T08:35:23Z)
• Synthesizing CTA Image Data for Type-B Aortic Dissection using Stable Diffusion Models [0.993378200812519]
  Stable Diffusion (SD) has gained a lot of attention in recent years in the field of Generative AI. It has been shown that Cardiac CTA images can be successfully generated using Text to Image (T2I) stable diffusion model.
  arXiv  Detail & Related papers  (2024-02-10T14:59:37Z)
• Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context [7.289988602420457]
  Diffusion probabilistic models (DDPMs) demonstrate superior image synthesis performance as compared to generative adversarial networks (GANs) These claims have been evaluated using either ensemble-based methods designed for natural images, or conventional measures of image quality such as structural similarity. The studies reveal new and important insights regarding the capacity of DDPMs to learn spatial context.
  arXiv  Detail & Related papers  (2023-09-19T17:58:35Z)
• NASDM: Nuclei-Aware Semantic Histopathology Image Generation Using Diffusion Models [3.2996723916635267]
  First-of-its-kind nuclei-aware semantic tissue generation framework (NASDM) NASDM can synthesize realistic tissue samples given a semantic instance mask of up to six different nuclei types. These synthetic images are useful in applications in pathology, validation of models, and supplementation of existing nuclei segmentation datasets.
  arXiv  Detail & Related papers  (2023-03-20T22:16:03Z)
• Enhanced Sharp-GAN For Histopathology Image Synthesis [63.845552349914186]
  Histopathology image synthesis aims to address the data shortage issue in training deep learning approaches for accurate cancer detection. We propose a novel approach that enhances the quality of synthetic images by using nuclei topology and contour regularization. The proposed approach outperforms Sharp-GAN in all four image quality metrics on two datasets.
  arXiv  Detail & Related papers  (2023-01-24T17:54:01Z)
• Data-driven generation of plausible tissue geometries for realistic photoacoustic image synthesis [53.65837038435433]
  Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties. We propose a novel approach to PAT data simulation, which we refer to as "learning to simulate" We leverage the concept of Generative Adversarial Networks (GANs) trained on semantically annotated medical imaging data to generate plausible tissue geometries.
  arXiv  Detail & Related papers  (2021-03-29T11:30:18Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Learning Neural Generative Dynamics for Molecular Conformation Generation [89.03173504444415]
  We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph. We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
  arXiv  Detail & Related papers  (2021-02-20T03:17:58Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.