Revealing Subtle Phenotypes in Small Microscopy Datasets Using Latent Diffusion Models

• URL: http://arxiv.org/abs/2502.09665v1
• Date: Wed, 12 Feb 2025 15:45:19 GMT
• Title: Revealing Subtle Phenotypes in Small Microscopy Datasets Using Latent Diffusion Models
• Authors: Anis Bourou, Biel Castaño Segade, Thomas Boye, Valérie Mezger, Auguste Genovesio,
• Abstract summary: We propose a novel approach that leverages pre-trained latent diffusion models to uncover subtle phenotypic changes. Our findings reveal that our approach enables effective detection of phenotypic variations, capturing both visually apparent and imperceptible differences.
• Score: 0.815557531820863
• License:
• Abstract: Identifying subtle phenotypic variations in cellular images is critical for advancing biological research and accelerating drug discovery. These variations are often masked by the inherent cellular heterogeneity, making it challenging to distinguish differences between experimental conditions. Recent advancements in deep generative models have demonstrated significant potential for revealing these nuanced phenotypes through image translation, opening new frontiers in cellular and molecular biology as well as the identification of novel biomarkers. Among these generative models, diffusion models stand out for their ability to produce high-quality, realistic images. However, training diffusion models typically requires large datasets and substantial computational resources, both of which can be limited in biological research. In this work, we propose a novel approach that leverages pre-trained latent diffusion models to uncover subtle phenotypic changes. We validate our approach qualitatively and quantitatively on several small datasets of microscopy images. Our findings reveal that our approach enables effective detection of phenotypic variations, capturing both visually apparent and imperceptible differences. Ultimately, our results highlight the promising potential of this approach for phenotype detection, especially in contexts constrained by limited data and computational capacity.

Related papers

• Diffusion Models for Molecules: A Survey of Methods and Tasks [56.44565051667812]
  Generative tasks about molecules are crucial for drug discovery and material design. Diffusion models have emerged as an impressive class of deep generative models. This paper conducts a comprehensive survey of diffusion model-based molecular generative methods.
  arXiv  Detail & Related papers  (2025-02-13T17:22:50Z)
• DiffEx: Explaining a Classifier with Diffusion Models to Identify Microscopic Cellular Variations [0.815557531820863]
  Discriminative deep learning models have excelled at classifying images into categories. These models are often perceived as black boxes due to their complexity and lack of interpretability. We propose DiffEx, a method for generating visually interpretable attributes to explain classifiers.
  arXiv  Detail & Related papers  (2025-02-12T12:46:58Z)
• G2PDiffusion: Genotype-to-Phenotype Prediction with Diffusion Models [108.94237816552024]
  This paper introduces G2PDiffusion, the first-of-its-kind diffusion model designed for genotype-to-phenotype generation across multiple species. We use images to represent morphological phenotypes across species and redefine phenotype prediction as conditional image generation.
  arXiv  Detail & Related papers  (2025-02-07T06:16:31Z)
• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• Feedback Efficient Online Fine-Tuning of Diffusion Models [52.170384048274364]
  We propose a novel reinforcement learning procedure that efficiently explores on the manifold of feasible samples. We present a theoretical analysis providing a regret guarantee, as well as empirical validation across three domains.
  arXiv  Detail & Related papers  (2024-02-26T07:24:32Z)
• Training Class-Imbalanced Diffusion Model Via Overlap Optimization [55.96820607533968]
  Diffusion models trained on real-world datasets often yield inferior fidelity for tail classes. Deep generative models, including diffusion models, are biased towards classes with abundant training images. We propose a method based on contrastive learning to minimize the overlap between distributions of synthetic images for different classes.
  arXiv  Detail & Related papers  (2024-02-16T16:47:21Z)
• PhenDiff: Revealing Subtle Phenotypes with Diffusion Models in Real Images [0.7329200485567825]
  PhenDiff identifies shifts in cellular phenotypes by translating a real image from one condition to another. We qualitatively and quantitatively validate this method on cases where the phenotypic changes are visible or invisible, such as in low concentrations of drug treatments.
  arXiv  Detail & Related papers  (2023-12-13T17:06:33Z)
• Tertiary Lymphoid Structures Generation through Graph-based Diffusion [54.37503714313661]
  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.
  arXiv  Detail & Related papers  (2023-10-10T14:37:17Z)
• Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action [16.800622727133252]
  Denoising diffusion models have emerged as one of the most powerful generative models in recent years. This review aims to provide a rather thorough overview of the applications of diffusion models in bioinformatics.
  arXiv  Detail & Related papers  (2023-02-13T15:37:23Z)
• Unpaired Image-to-Image Translation with Limited Data to Reveal Subtle Phenotypes [0.5076419064097732]
  We present an improved CycleGAN architecture that employs self-supervised discriminators to alleviate the need for numerous images. We also provide results obtained with small biological datasets on obvious and non-obvious cell phenotype variations.
  arXiv  Detail & Related papers  (2023-01-21T16:25:04Z)

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.