Tertiary Lymphoid Structures Generation through Graph-based Diffusion

• URL: http://arxiv.org/abs/2310.06661v1
• Date: Tue, 10 Oct 2023 14:37:17 GMT
• Title: Tertiary Lymphoid Structures Generation through Graph-based Diffusion
• Authors: Manuel Madeira, Dorina Thanou, Pascal Frossard
• Abstract summary: In this work, we leverage state-of-the-art graph-based diffusion models to generate biologically meaningful cell-graphs. We show that the adopted graph diffusion model is able to accurately learn the distribution of cells in terms of their tertiary lymphoid structures (TLS) content.
• Score: 54.37503714313661
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph-based representation approaches have been proven to be successful in the analysis of biomedical data, due to their capability of capturing intricate dependencies between biological entities, such as the spatial organization of different cell types in a tumor tissue. However, to further enhance our understanding of the underlying governing biological mechanisms, it is important to accurately capture the actual distributions of such complex data. Graph-based deep generative models are specifically tailored to accomplish that. In this work, we leverage state-of-the-art graph-based diffusion models to generate biologically meaningful cell-graphs. In particular, we show that the adopted graph diffusion model is able to accurately learn the distribution of cells in terms of their tertiary lymphoid structures (TLS) content, a well-established biomarker for evaluating the cancer progression in oncology research. Additionally, we further illustrate the utility of the learned generative models for data augmentation in a TLS classification task. To the best of our knowledge, this is the first work that leverages the power of graph diffusion models in generating meaningful biological cell structures.

Related papers

• HistoSmith: Single-Stage Histology Image-Label Generation via Conditional Latent Diffusion for Enhanced Cell Segmentation and Classification [0.19791587637442667]
  This study introduces a novel single-stage approach for generating image-label pairs to augment histology datasets. Unlike state-of-the-art methods that utilize diffusion models with separate components for label and image generation, our approach employs a latent diffusion model. This model enables tailored data generation by conditioning on user-defined parameters such as cell types, quantities, and tissue types.
  arXiv  Detail & Related papers  (2025-02-12T19:51:41Z)
• HOG-Diff: Higher-Order Guided Diffusion for Graph Generation [16.879154374481235]
  Graph generation is a critical yet challenging task as empirical analyses require a deep understanding of complex, non-Euclidean structures. We propose a novel Higher-order Guided Diffusion model that follows a coarse-to-fine generation curriculum and is guided by higher-order information. Our model exhibits a stronger theoretical guarantee than classical diffusion frameworks.
  arXiv  Detail & Related papers  (2025-02-06T18:51:14Z)
• An Organism Starts with a Single Pix-Cell: A Neural Cellular Diffusion for High-Resolution Image Synthesis [8.01395073111961]
  We introduce a novel family of models termed Generative Cellular Automata (GeCA) GeCAs are evaluated as an effective augmentation tool for retinal disease classification across two imaging modalities: Fundus and Optical Coherence Tomography ( OCT) In the context of OCT imaging, where data is scarce and the distribution of classes is inherently skewed, GeCA significantly boosts the performance of 11 different ophthalmological conditions.
  arXiv  Detail & Related papers  (2024-07-03T11:26:09Z)
• Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis [7.996257103473235]
  We propose Pathology-Genome Heterogeneous Graph (PGHG) that integrates whole slide images (WSI) and bulk RNA-Seq expression data with heterogeneous graph neural network for cancer survival analysis. The PGHG consists of biological knowledge-guided representation learning network and pathology-genome heterogeneous graph. We evaluate the model on low-grade gliomas, glioblastoma, and kidney renal papillary cell carcinoma datasets from the Cancer Genome Atlas.
  arXiv  Detail & Related papers  (2024-04-11T09:07:40Z)
• Neural Echos: Depthwise Convolutional Filters Replicate Biological Receptive Fields [56.69755544814834]
  We present evidence suggesting that depthwise convolutional kernels are effectively replicating the biological receptive fields observed in the mammalian retina. We propose a scheme that draws inspiration from the biological receptive fields.
  arXiv  Detail & Related papers  (2024-01-18T18:06:22Z)
• Graph Relation Distillation for Efficient Biomedical Instance Segmentation [80.51124447333493]
  We propose a graph relation distillation approach for efficient biomedical instance segmentation. We introduce two graph distillation schemes deployed at both the intra-image level and the inter-image level. Experimental results on a number of biomedical datasets validate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-01-12T04:41:23Z)
• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations [68.32093648671496]
  We introduce GODE, which accounts for the dual-level structure inherent in molecules. Molecules possess an intrinsic graph structure and simultaneously function as nodes within a broader molecular knowledge graph. By pre-training two GNNs on different graph structures, GODE effectively fuses molecular structures with their corresponding knowledge graph substructures.
  arXiv  Detail & Related papers  (2023-06-02T15:49:45Z)
• Studying Limits of Explainability by Integrated Gradients for Gene Expression Models [3.220287168504093]
  We show that ranking features by importance is not enough to robustly identify biomarkers. As it is difficult to evaluate whether biomarkers reflect relevant causes without known ground truth, we simulate gene expression data by proposing a hierarchical model.
  arXiv  Detail & Related papers  (2023-03-19T19:54:15Z)
• Self-Supervised Graph Representation Learning for Neuronal Morphologies [75.38832711445421]
  We present GraphDINO, a data-driven approach to learn low-dimensional representations of 3D neuronal morphologies from unlabeled datasets. We show, in two different species and across multiple brain areas, that this method yields morphological cell type clusterings on par with manual feature-based classification by experts. Our method could potentially enable data-driven discovery of novel morphological features and cell types in large-scale datasets.
  arXiv  Detail & Related papers  (2021-12-23T12:17:47Z)

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.