IgCONDA-PET: Implicitly-Guided Counterfactual Diffusion for Detecting Anomalies in PET Images

• URL: http://arxiv.org/abs/2405.00239v1
• Date: Tue, 30 Apr 2024 23:09:54 GMT
• Title: IgCONDA-PET: Implicitly-Guided Counterfactual Diffusion for Detecting Anomalies in PET Images
• Authors: Shadab Ahamed, Yixi Xu, Arman Rahmim,
• Abstract summary: Minimizing the need for pixel-level annotated data for training PET anomaly segmentation networks is crucial. We present a weakly supervised and Implicitly guided COuNterfactual diffusion model for Detecting Anomalies in PET images.
• Score: 0.840320502420283
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Minimizing the need for pixel-level annotated data for training PET anomaly segmentation networks is crucial, particularly due to time and cost constraints related to expert annotations. Current un-/weakly-supervised anomaly detection methods rely on autoencoder or generative adversarial networks trained only on healthy data, although these are more challenging to train. In this work, we present a weakly supervised and Implicitly guided COuNterfactual diffusion model for Detecting Anomalies in PET images, branded as IgCONDA-PET. The training is conditioned on image class labels (healthy vs. unhealthy) along with implicit guidance to generate counterfactuals for an unhealthy image with anomalies. The counterfactual generation process synthesizes the healthy counterpart for a given unhealthy image, and the difference between the two facilitates the identification of anomaly locations. The code is available at: https://github.com/igcondapet/IgCONDA-PET.git

Related papers

• PET Synthesis via Self-supervised Adaptive Residual Estimation Generative Adversarial Network [14.381830012670969]
  Recent methods to generate high-quality PET images from low-dose counterparts have been reported to be state-of-the-art for low-to-high image recovery methods. To address these issues, we developed a self-supervised adaptive residual estimation generative adversarial network (SS-AEGAN) SS-AEGAN consistently outperformed the state-of-the-art synthesis methods with various dose reduction factors.
  arXiv  Detail & Related papers  (2023-10-24T06:43:56Z)
• Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction [62.29541106695824]
  This paper presents a coarse-to-fine PET reconstruction framework that consists of a coarse prediction module (CPM) and an iterative refinement module (IRM) By delegating most of the computational overhead to the CPM, the overall sampling speed of our method can be significantly improved. Two additional strategies, i.e., an auxiliary guidance strategy and a contrastive diffusion strategy, are proposed and integrated into the reconstruction process.
  arXiv  Detail & Related papers  (2023-08-20T04:10:36Z)
• CG-3DSRGAN: A classification guided 3D generative adversarial network for image quality recovery from low-dose PET images [10.994223928445589]
  High radioactivity caused by the injected tracer dose is a major concern in PET imaging. Reducing the dose leads to inadequate image quality for diagnostic practice. CNNs-based methods have been developed for high quality PET synthesis from its low-dose counterparts.
  arXiv  Detail & Related papers  (2023-04-03T05:39:02Z)
• Seamless Iterative Semi-Supervised Correction of Imperfect Labels in Microscopy Images [57.42492501915773]
  In-vitro tests are an alternative to animal testing for the toxicity of medical devices. Human fatigue plays a role in error making, making the use of deep learning appealing. We propose Seamless Iterative Semi-Supervised correction of Imperfect labels (SISSI) Our method successfully provides an adaptive early learning correction technique for object detection.
  arXiv  Detail & Related papers  (2022-08-05T18:52:20Z)
• Self-Supervised Learning as a Means To Reduce the Need for Labeled Data in Medical Image Analysis [64.4093648042484]
  We use a dataset of chest X-ray images with bounding box labels for 13 different classes of anomalies. We show that it is possible to achieve similar performance to a fully supervised model in terms of mean average precision and accuracy with only 60% of the labeled data.
  arXiv  Detail & Related papers  (2022-06-01T09:20:30Z)
• FlowNet-PET: Unsupervised Learning to Perform Respiratory Motion Correction in PET Imaging [11.451728125088113]
  FlowNet-PET is an interpretable and unsupervised deep learning technique to correct for breathing motion in PET imaging. As a proof-of-concept, FlowNet-PET was applied to anthropomorphic digital phantom data.
  arXiv  Detail & Related papers  (2022-05-27T18:18:19Z)
• Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
  We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T08:41:17Z)
• Improving and Simplifying Pattern Exploiting Training [81.77863825517511]
  Pattern Exploiting Training (PET) is a recent approach that leverages patterns for few-shot learning. In this paper, we focus on few shot learning without any unlabeled data and introduce ADAPET. ADAPET outperforms PET on SuperGLUE without any task-specific unlabeled data.
  arXiv  Detail & Related papers  (2021-03-22T15:52:45Z)
• ASL to PET Translation by a Semi-supervised Residual-based Attention-guided Convolutional Neural Network [3.2480194378336464]
  Arterial Spin Labeling (ASL) MRI is a non-invasive, non-radioactive, and relatively cheap imaging technique for brain hemodynamic measurements. We propose a convolutional neural network (CNN) based model for translating ASL to PET images.
  arXiv  Detail & Related papers  (2021-03-08T22:06:02Z)
• 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)
• RADIOGAN: Deep Convolutional Conditional Generative adversarial Network To Generate PET Images [3.947298454012977]
  We propose a deep convolutional conditional generative adversarial network to generate MIP positron emission tomography image (PET) The advantage of our proposed method consists of one model that is capable of generating different classes of lesions trained on a small sample size for each class of lesion. In addition, we show that a walk through a latent space can be used as a tool to evaluate the images generated.
  arXiv  Detail & Related papers  (2020-03-19T10:14:40Z)

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.