Unsupervised Contrastive Analysis for Salient Pattern Detection using Conditional Diffusion Models

• URL: http://arxiv.org/abs/2406.00772v2
• Date: Tue, 4 Jun 2024 08:53:24 GMT
• Title: Unsupervised Contrastive Analysis for Salient Pattern Detection using Conditional Diffusion Models
• Authors: Cristiano Patrício, Carlo Alberto Barbano, Attilio Fiandrotti, Riccardo Renzulli, Marco Grangetto, Luis F. Teixeira, João C. Neves,
• Abstract summary: Contrastive Analysis (CA) aims to identify patterns in images that allow distinguishing between a background (BG) dataset and a target (TG) dataset (i.e. unhealthy subjects) Recent works on this topic rely on variational autoencoders (VAE) or contrastive learning strategies to learn the patterns that separate TG samples from BG samples in a supervised manner. We employ a self-supervised contrastive encoder to learn a latent representation encoding only common patterns from input images, using samples exclusively from the BG dataset during training, and approximating the distribution of the target patterns by leveraging data augmentation techniques.
• Score: 13.970483987621135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrastive Analysis (CA) regards the problem of identifying patterns in images that allow distinguishing between a background (BG) dataset (i.e. healthy subjects) and a target (TG) dataset (i.e. unhealthy subjects). Recent works on this topic rely on variational autoencoders (VAE) or contrastive learning strategies to learn the patterns that separate TG samples from BG samples in a supervised manner. However, the dependency on target (unhealthy) samples can be challenging in medical scenarios due to their limited availability. Also, the blurred reconstructions of VAEs lack utility and interpretability. In this work, we redefine the CA task by employing a self-supervised contrastive encoder to learn a latent representation encoding only common patterns from input images, using samples exclusively from the BG dataset during training, and approximating the distribution of the target patterns by leveraging data augmentation techniques. Subsequently, we exploit state-of-the-art generative methods, i.e. diffusion models, conditioned on the learned latent representation to produce a realistic (healthy) version of the input image encoding solely the common patterns. Thorough validation on a facial image dataset and experiments across three brain MRI datasets demonstrate that conditioning the generative process of state-of-the-art generative methods with the latent representation from our self-supervised contrastive encoder yields improvements in the generated image quality and in the accuracy of image classification. The code is available at https://github.com/CristianoPatricio/unsupervised-contrastive-cond-diff.

Related papers

• Latent Diffusion Model for Medical Image Standardization and Enhancement [11.295078152769559]
  DiffusionCT is a score-based DDPM model that transforms disparate non-standard distributions into a standardized form. The architecture comprises a U-Net-based encoder-decoder, augmented by a DDPM model integrated at the bottleneck position. Empirical tests on patient CT images indicate notable improvements in image standardization using DiffusionCT.
  arXiv  Detail & Related papers  (2023-10-08T17:11:14Z)
• DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data Augmentation [48.25619775814776]
  This paper proposes DiffAug, a novel unsupervised contrastive learning technique with diffusion mode-based positive data generation. DiffAug consists of a semantic encoder and a conditional diffusion model; the conditional diffusion model generates new positive samples conditioned on the semantic encoding. Experimental evaluations show that DiffAug outperforms hand-designed and SOTA model-based augmentation methods on DNA sequence, visual, and bio-feature datasets.
  arXiv  Detail & Related papers  (2023-09-10T13:28:46Z)
• ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models [69.9178140563928]
  Colonoscopy analysis is essential for assisting clinical diagnosis and treatment. The scarcity of annotated data limits the effectiveness and generalization of existing methods. We propose an Adaptive Refinement Semantic Diffusion Model (ArSDM) to generate colonoscopy images that benefit the downstream tasks.
  arXiv  Detail & Related papers  (2023-09-03T07:55:46Z)
• Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks [64.67735676127208]
  Text-to-image diffusion models have shown great potential for benefiting image recognition. Although promising, there has been inadequate exploration dedicated to unsupervised learning on diffusion-generated images. We introduce customized solutions by fully exploiting the aforementioned free attention masks.
  arXiv  Detail & Related papers  (2023-08-13T10:07:46Z)
• VAESim: A probabilistic approach for self-supervised prototype discovery [0.23624125155742057]
  We propose an architecture for image stratification based on a conditional variational autoencoder. We use a continuous latent space to represent the continuum of disorders and find clusters during training, which can then be used for image/patient stratification. We demonstrate that our method outperforms baselines in terms of kNN accuracy measured on a classification task against a standard VAE.
  arXiv  Detail & Related papers  (2022-09-25T17:55:31Z)
• Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models [1.6352599467675781]
  We propose a method based on diffusion models to detect and segment anomalies in brain imaging. Our diffusion models achieve competitive performance compared with autoregressive approaches across a series of experiments with 2D CT and MRI data.
  arXiv  Detail & Related papers  (2022-06-07T17:30:43Z)
• Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images [23.79184121052212]
  Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i.e., healthy) images. We propose a novel self-supervised representation learning method, called Constrained Contrastive Distribution learning for anomaly detection (CCD) Our method outperforms current state-of-the-art UAD approaches on three different colonoscopy and fundus screening datasets.
  arXiv  Detail & Related papers  (2021-03-05T01:56:58Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)

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.