DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms using Self-adversarial Learning

• URL: http://arxiv.org/abs/2307.02935v1
• Date: Thu, 6 Jul 2023 11:52:36 GMT
• Title: DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms using Self-adversarial Learning
• Authors: Xin Wang, Tao Tan, Yuan Gao, Luyi Han, Tianyu Zhang, Chunyao Lu, Regina Beets-Tan, Ruisheng Su, Ritse Mann
• Abstract summary: We propose a novel framework, DisAsymNet, which utilizes asymmetrical abnormality transformer guided self-adversarial learning for disentangling abnormalities and symmetric Bi-MG. We conduct experiments on three public and one in-house dataset, and demonstrate that our method outperforms existing methods in abnormality classification, segmentation, and localization tasks.
• Score: 14.482657073587188
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Asymmetry is a crucial characteristic of bilateral mammograms (Bi-MG) when abnormalities are developing. It is widely utilized by radiologists for diagnosis. The question of 'what the symmetrical Bi-MG would look like when the asymmetrical abnormalities have been removed ?' has not yet received strong attention in the development of algorithms on mammograms. Addressing this question could provide valuable insights into mammographic anatomy and aid in diagnostic interpretation. Hence, we propose a novel framework, DisAsymNet, which utilizes asymmetrical abnormality transformer guided self-adversarial learning for disentangling abnormalities and symmetric Bi-MG. At the same time, our proposed method is partially guided by randomly synthesized abnormalities. We conduct experiments on three public and one in-house dataset, and demonstrate that our method outperforms existing methods in abnormality classification, segmentation, and localization tasks. Additionally, reconstructed normal mammograms can provide insights toward better interpretable visual cues for clinical diagnosis. The code will be accessible to the public.

Related papers

• Spatial-aware Attention Generative Adversarial Network for Semi-supervised Anomaly Detection in Medical Image [63.59114880750643]
  We introduce a novel Spatial-aware Attention Generative Adrialversa Network (SAGAN) for one-class semi-supervised generation of health images. SAGAN generates high-quality health images corresponding to unlabeled data, guided by the reconstruction of normal images and restoration of pseudo-anomaly images. Extensive experiments on three medical datasets demonstrate that the proposed SAGAN outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-05-21T15:41:34Z)
• Towards frugal unsupervised detection of subtle abnormalities in medical imaging [0.0]
  Anomaly detection in medical imaging is a challenging task in contexts where abnormalities are not annotated. We investigate mixtures of probability distributions whose versatility has been widely recognized. This online approach is illustrated on the challenging detection of subtle abnormalities in MR brain scans for the follow-up of newly diagnosed Parkinsonian patients.
  arXiv  Detail & Related papers  (2023-09-04T07:44:54Z)
• Industrial and Medical Anomaly Detection Through Cycle-Consistent Adversarial Networks [0.6554326244334868]
  A new Anomaly Detection (AD) approach for industrial and medical images is proposed. The proposed method uses Cycle-Generative Adrial Networks (Cycle-GAN) for (ab)normal-to-normal translation. Results demonstrate accurate performance with a zero false negative constraint compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-02-10T10:25:12Z)
• Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images [29.57501199670898]
  We introduce one-class semi-supervised learning (OC-SSL) to utilize known normal and unlabeled images for training. Ensembles of reconstruction networks are designed to model the distribution of normal images and the distribution of both normal and unlabeled images. We propose a new perspective on self-supervised learning, which is designed to refine the anomaly scores rather than detect anomalies directly.
  arXiv  Detail & Related papers  (2022-10-09T11:18:45Z)
• StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder [48.2010192865749]
  Unsupervised anomaly detection (UAD) can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out of distribution samples. This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA) The proposed pipeline achieved a Dice score of 0.642$pm$0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859$pm$0.112 while detecting artificially induced anomalies.
  arXiv  Detail & Related papers  (2022-01-31T14:27:35Z)
• Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-Contrast CT Images [50.55978219682419]
  We propose a symmetry enhanced attention network (SEAN) for acute ischemic infarct segmentation. Our proposed network automatically transforms an input CT image into the standard space where the brain tissue is bilaterally symmetric. The proposed SEAN outperforms some symmetry-based state-of-the-art methods in terms of both dice coefficient and infarct localization.
  arXiv  Detail & Related papers  (2021-10-11T07:13:26Z)
• Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection [49.14070210387509]
  We propose an Anatomy-aware Graph convolutional Network (AGN) for mammogram mass detection. AGN is tailored for mammogram mass detection and endows existing detection methods with multi-view reasoning ability. Experiments on two standard benchmarks reveal that AGN significantly exceeds the state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-05-21T06:48:34Z)
• Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification [48.4619620405991]
  Mammogram benign or malignant classification with only image-level labels is challenging due to the absence of lesion annotations. Motivated by the symmetric prior, we can explore a counterfactual problem that how would the features have behaved if there were no lesions in the image. We derive a new theoretical result for counterfactual generation based on the symmetric prior.
  arXiv  Detail & Related papers  (2020-09-30T03:15:30Z)
• 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)
• Anatomy-Aware Siamese Network: Exploiting Semantic Asymmetry for Accurate Pelvic Fracture Detection in X-ray Images [36.35987775099686]
  We propose a novel fracture detection framework that builds upon a Siamese network enhanced with a spatial transformer layer. Our proposed method have been extensively evaluated on 2,359 PXRs from unique patients. This is the highest among state-of-the-art fracture detection methods, with improved clinical indications.
  arXiv  Detail & Related papers  (2020-07-03T02:33:24Z)

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.