DiffEx: Explaining a Classifier with Diffusion Models to Identify Microscopic Cellular Variations

• URL: http://arxiv.org/abs/2502.09663v1
• Date: Wed, 12 Feb 2025 12:46:58 GMT
• Title: DiffEx: Explaining a Classifier with Diffusion Models to Identify Microscopic Cellular Variations
• Authors: Anis Bourou, Saranga Kingkor Mahanta, Thomas Boyer, Valérie Mezger, Auguste Genovesio,
• Abstract summary: 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.
• Score: 0.815557531820863
• License:
• Abstract: In recent years, deep learning models have been extensively applied to biological data across various modalities. Discriminative deep learning models have excelled at classifying images into categories (e.g., healthy versus diseased, treated versus untreated). However, these models are often perceived as black boxes due to their complexity and lack of interpretability, limiting their application in real-world biological contexts. In biological research, explainability is essential: understanding classifier decisions and identifying subtle differences between conditions are critical for elucidating the effects of treatments, disease progression, and biological processes. To address this challenge, we propose DiffEx, a method for generating visually interpretable attributes to explain classifiers and identify microscopic cellular variations between different conditions. We demonstrate the effectiveness of DiffEx in explaining classifiers trained on natural and biological images. Furthermore, we use DiffEx to uncover phenotypic differences within microscopy datasets. By offering insights into cellular variations through classifier explanations, DiffEx has the potential to advance the understanding of diseases and aid drug discovery by identifying novel biomarkers.

Related papers

• Revealing Subtle Phenotypes in Small Microscopy Datasets Using Latent Diffusion Models [0.815557531820863]
  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.
  arXiv  Detail & Related papers  (2025-02-12T15:45:19Z)
• Causal Representation Learning from Multimodal Biological Observations [57.00712157758845]
  We aim to develop flexible identification conditions for multimodal data. We establish identifiability guarantees for each latent component, extending the subspace identification results from prior work. Our key theoretical ingredient is the structural sparsity of the causal connections among distinct modalities.
  arXiv  Detail & Related papers  (2024-11-10T16:40:27Z)
• Diff-Def: Diffusion-Generated Deformation Fields for Conditional Atlases [13.440406411539987]
  Conditional atlases allow for the investigation of fine-grained anatomical differences. We use latent diffusion models to generate deformation fields, which transform a general population atlas into a specific sub-population. We compare our method to several state-of-the-art atlas generation methods in experiments using 5000 brain as well as whole-body MR images from UK Biobank.
  arXiv  Detail & Related papers  (2024-03-25T13:52:48Z)
• 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)
• Finding Interpretable Class-Specific Patterns through Efficient Neural Search [43.454121220860564]
  We propose a novel, inherently interpretable binary neural network architecture DNAPS that extracts differential patterns from data. DiffNaps is scalable to hundreds of thousands of features and robust to noise. We show on synthetic and real world data, including three biological applications, that, unlike its competitors, DiffNaps consistently yields accurate, succinct, and interpretable class descriptions.
  arXiv  Detail & Related papers  (2023-12-07T14:09:18Z)
• 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)
• Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images [52.527733226555206]
  We investigate the use of four attribution methods to explain a multiple instance learning models. We study two datasets of acute myeloid leukemia with over 100 000 single cell images. We compare attribution maps with the annotations of a medical expert to see how the model's decision-making differs from the human standard.
  arXiv  Detail & Related papers  (2023-03-15T14:00:11Z)
• Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unlabeled, unannotated pathology slides [9.27127895781971]
  Histomorphological Phenotype Learning operates via the automatic discovery of discriminatory image features in small image tiles. Tiles are grouped into morphologically similar clusters which constitute a library of histomorphological phenotypes.
  arXiv  Detail & Related papers  (2022-05-04T08:06:55Z)
• Discriminative Attribution from Counterfactuals [64.94009515033984]
  We present a method for neural network interpretability by combining feature attribution with counterfactual explanations. We show that this method can be used to quantitatively evaluate the performance of feature attribution methods in an objective manner.
  arXiv  Detail & Related papers  (2021-09-28T00:53:34Z)
• Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition [65.77962788209103]
  We propose class subset learning by dividing the long-tailed data into multiple class subsets according to prior knowledge. It enforces the model to focus on learning the subset-specific knowledge. The proposed framework proved to be effective for the long-tailed retinal diseases recognition task.
  arXiv  Detail & Related papers  (2021-04-22T13:39:33Z)

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.