Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification

• URL: http://arxiv.org/abs/2009.14406v1
• Date: Wed, 30 Sep 2020 03:15:30 GMT
• Title: Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification
• Authors: Chu-ran Wang, Jing Li, Fandong Zhang, Xinwei Sun, Hao Dong, Yizhou Yu, Yizhou Wang
• Abstract summary: 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.
• Score: 48.4619620405991
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mammogram benign or malignant classification with only image-level labels is challenging due to the absence of lesion annotations. Motivated by the symmetric prior that the lesions on one side of breasts rarely appear in the corresponding areas on the other side, given a diseased image, we can explore a counterfactual problem that how would the features have behaved if there were no lesions in the image, so as to identify the lesion areas. We derive a new theoretical result for counterfactual generation based on the symmetric prior. By building a causal model that entails such a prior for bilateral images, we obtain two optimization goals for counterfactual generation, which can be accomplished via our newly proposed counterfactual generative network. Our proposed model is mainly composed of Generator Adversarial Network and a \emph{prediction feedback mechanism}, they are optimized jointly and prompt each other. Specifically, the former can further improve the classification performance by generating counterfactual features to calculate lesion areas. On the other hand, the latter helps counterfactual generation by the supervision of classification loss. The utility of our method and the effectiveness of each module in our model can be verified by state-of-the-art performance on INBreast and an in-house dataset and ablation studies.

Related papers

• The Topology-Overlap Trade-Off in Retinal Arteriole-Venule Segmentation [0.0]
  convolutional neural networks can achieve high overlap between predictions and expert annotations. We show that our model is able to produce results on par with state-of-the-art from the point of view of overlap.
  arXiv  Detail & Related papers  (2023-03-31T13:01:05Z)
• Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection [8.737589725372398]
  We introduce a novel unsupervised approach, called PHANES (Pseudo Healthy generative networks for ANomaly) Our method has the capability of reversing anomalies, preserving healthy tissue and replacing anomalous regions with pseudo-healthy reconstructions. We demonstrate the effectiveness of PHANES in detecting stroke lesions in T1w brain MRI datasets and show significant improvements over state-of-the-art (SOTA) methods.
  arXiv  Detail & Related papers  (2023-03-15T08:54:20Z)
• Unsupervised Anomaly Appraisal of Cleft Faces Using a StyleGAN2-based Model Adaptation Technique [5.224306534441244]
  This paper presents a novel machine learning framework to consistently detect, localize and rate congenital cleft lip anomalies in human faces. The proposed method employs the StyleGAN2 generative adversarial network with model adaptation to produce normalized transformations of cleft-affected faces. The anomaly scores yielded by the proposed computer model correlate closely with the human ratings of facial differences, leading to 0.942 Pearson's r score.
  arXiv  Detail & Related papers  (2022-11-12T13:30:20Z)
• 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)
• 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)
• Malignancy Prediction and Lesion Identification from Clinical Dermatological Images [65.1629311281062]
  We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images. We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
  arXiv  Detail & Related papers  (2021-04-02T20:52:05Z)
• 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)
• 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)
• Unsupervised Lesion Detection via Image Restoration with a Normative Prior [6.495883501989547]
  We propose a probabilistic model that uses a network-based prior as the normative distribution and detect lesions pixel-wise using MAP estimation. Experiments with gliomas and stroke lesions in brain MRI show that the proposed approach outperforms the state-of-the-art unsupervised methods by a substantial margin.
  arXiv  Detail & Related papers  (2020-04-30T18:03:18Z)

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.