Surreal-GAN:Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patterns

• URL: http://arxiv.org/abs/2205.04523v1
• Date: Mon, 9 May 2022 19:09:28 GMT
• Title: Surreal-GAN:Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patterns
• Authors: Zhijian Yang, Junhao Wen, Christos Davatzikos
• Abstract summary: We propose Surreal-GAN (Semi-SUpeRvised ReprEsentAtion Learning via GAN) to model disease effects. We validated the model through extensive semi-synthetic experiments, and then demonstrate its potential in capturing biologically plausible imaging patterns in Alzheimer's disease.
• Score: 4.965264481651854
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A plethora of machine learning methods have been applied to imaging data, enabling the construction of clinically relevant imaging signatures of neurological and neuropsychiatric diseases. Oftentimes, such methods don't explicitly model the heterogeneity of disease effects, or approach it via nonlinear models that are not interpretable. Moreover, unsupervised methods may parse heterogeneity that is driven by nuisance confounding factors that affect brain structure or function, rather than heterogeneity relevant to a pathology of interest. On the other hand, semi-supervised clustering methods seek to derive a dichotomous subtype membership, ignoring the truth that disease heterogeneity spatially and temporally extends along a continuum. To address the aforementioned limitations, herein, we propose a novel method, termed Surreal-GAN (Semi-SUpeRvised ReprEsentAtion Learning via GAN). Using cross-sectional imaging data, Surreal-GAN dissects underlying disease-related heterogeneity under the principle of semi-supervised clustering (cluster mappings from normal control to patient), proposes a continuously dimensional representation, and infers the disease severity of patients at individual level along each dimension. The model first learns a transformation function from normal control (CN) domain to the patient (PT) domain with latent variables controlling transformation directions. An inverse mapping function together with regularization on function continuity, pattern orthogonality and monotonicity was also imposed to make sure that the transformation function captures necessarily meaningful imaging patterns with clinical significance. We first validated the model through extensive semi-synthetic experiments, and then demonstrate its potential in capturing biologically plausible imaging patterns in Alzheimer's disease (AD).

Related papers

• Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRI [2.5847188023177403]
  Unsupervised anomaly segmentation aims to detect patterns that are distinct from any patterns processed during training. This paper introduces Masked Modality Cycles with Conditional Diffusion (MMCCD), a method that enables segmentation of anomalies across diverse patterns in multimodal MRI. We show that our method compares favorably to previous unsupervised approaches based on image reconstruction and denoising with autoencoders and diffusion models.
  arXiv  Detail & Related papers  (2023-08-30T17:16:02Z)
• Conditionally Invariant Representation Learning for Disentangling Cellular Heterogeneity [25.488181126364186]
  This paper presents a novel approach that leverages domain variability to learn representations that are conditionally invariant to unwanted variability or distractors. We apply our method to grand biological challenges, such as data integration in single-cell genomics. Specifically, the proposed approach helps to disentangle biological signals from data biases that are unrelated to the target task or the causal explanation of interest.
  arXiv  Detail & Related papers  (2023-07-02T12:52:41Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Domain Invariant Model with Graph Convolutional Network for Mammogram Classification [49.691629817104925]
  We propose a novel framework, namely Domain Invariant Model with Graph Convolutional Network (DIM-GCN) We first propose a Bayesian network, which explicitly decomposes the latent variables into disease-related and other disease-irrelevant parts that are provable to be disentangled from each other. To better capture the macroscopic features, we leverage the observed clinical attributes as a goal for reconstruction, via Graph Convolutional Network (GCN)
  arXiv  Detail & Related papers  (2022-04-21T08:23:44Z)
• 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)
• ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans [3.589107822343127]
  We take advantage of recent developments in generative deep learning to develop a method for simultaneous classification, or regression, and feature attribution. We validate our method on the tasks of Mini-Mental State Examination (MMSE) cognitive test score prediction for the Alzheimer's Disease Neuroimaging Initiative cohort. We show that the generated FA maps can be used to explain outlier predictions and demonstrate that the inclusion of a regression module improves the disentanglement of the latent space.
  arXiv  Detail & Related papers  (2021-03-03T17:55:14Z)
• Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models [16.218680291606628]
  Interpretability is a critical factor in applying complex deep learning models to advance the understanding of brain disorders. We propose to train simulator networks that can warp a given image to inject or remove patterns of the disease. We apply our approach to interpreting classifiers trained on a synthetic dataset and two neuroimaging datasets to visualize the effect of the Alzheimer's disease and alcohol use disorder.
  arXiv  Detail & Related papers  (2021-02-16T15:57:37Z)
• 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)
• Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation [79.58311369297635]
  We propose a new weakly-supervised lesions transfer framework, which can explore transferable domain-invariant knowledge across different datasets. A Wasserstein quantified transferability framework is developed to highlight widerange transferable contextual dependencies. A novel self-supervised pseudo label generator is designed to equally provide confident pseudo pixel labels for both hard-to-transfer and easy-to-transfer target samples.
  arXiv  Detail & Related papers  (2020-12-08T02:26:03Z)
• Smile-GANs: Semi-supervised clustering via GANs for dissecting brain disease heterogeneity from medical images [4.965264481651854]
  We propose Smile-GANs (SeMi-supervIsed cLustEring via GANs), for semi-supervised clustering, and apply it to brain MRI scans. Smile-GANs first learns multiple distinct mappings by generating PT from CN, with each mapping characterizing one relatively distinct pathological pattern. Using relaxed assumptions on PT/CN data distribution and imposing mapping non-linearity, Smile-GANs captures heterogeneous differences in distribution between the CN and PT domains.
  arXiv  Detail & Related papers  (2020-06-27T02:06:21Z)
• What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation [51.7837386041158]
  We develop a new unsupervised semantic transfer model including two complementary modules for endoscopic lesions segmentation. Specifically, T_D focuses on where to translate transferable visual information of medical lesions via residual transferability-aware bottleneck. T_F highlights how to augment transferable semantic features of various lesions and automatically ignore untransferable representations.
  arXiv  Detail & Related papers  (2020-04-24T00:57:05Z)

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.