FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss

• URL: http://arxiv.org/abs/2201.12589v1
• Date: Sat, 29 Jan 2022 13:45:39 GMT
• Title: FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss
• Authors: Guoyang Xie, Jinbao Wang, Yawen Huang, Yefeng Zheng, Feng Zheng, Yaochu Jin
• Abstract summary: We propose a novel self-supervised learning (FedMed) for brain image synthesis. An affine transform loss (ATL) was formulated to make use of severely distorted images without violating privacy legislation. The proposed method demonstrates advanced performance in both the quality of synthesized results under a severely misaligned and unpaired data setting.
• Score: 58.58979566599889
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The existence of completely aligned and paired multi-modal neuroimaging data has proved its effectiveness in the diagnosis of brain diseases. However, collecting the full set of well-aligned and paired data is impractical or even luxurious, since the practical difficulties may include high cost, long time acquisition, image corruption, and privacy issues. Previously, the misaligned unpaired neuroimaging data (termed as MUD) are generally treated as noisy label. However, such a noisy label-based method could not work very well when misaligned data occurs distortions severely, for example, different angles of rotation. In this paper, we propose a novel federated self-supervised learning (FedMed) for brain image synthesis. An affine transform loss (ATL) was formulated to make use of severely distorted images without violating privacy legislation for the hospital. We then introduce a new data augmentation procedure for self-supervised training and fed it into three auxiliary heads, namely auxiliary rotation, auxiliary translation, and auxiliary scaling heads. The proposed method demonstrates advanced performance in both the quality of synthesized results under a severely misaligned and unpaired data setting, and better stability than other GAN-based algorithms. The proposed method also reduces the demand for deformable registration while encouraging to realize the usage of those misaligned and unpaired data. Experimental results verify the outstanding ability of our learning paradigm compared to other state-of-the-art approaches. Our code is available on the website: https://github.com/FedMed-Meta/FedMed-ATL

Related papers

• Adversarial Distortion Learning for Medical Image Denoising [43.53912137735094]
  We present a novel adversarial distortion learning (ADL) for denoising two- and three-dimensional (2D/3D) biomedical image data. The proposed ADL consists of two auto-encoders: a denoiser and a discriminator. Both the denoiser and the discriminator are built upon a proposed auto-encoder called Efficient-Unet.
  arXiv  Detail & Related papers  (2022-04-29T13:47:39Z)
• MIPR:Automatic Annotation of Medical Images with Pixel Rearrangement [7.39560318487728]
  We pro?pose a novel approach to solve the lack of annotated data from another angle, called medical image pixel rearrangement (short in MIPR) The MIPR combines image-editing and pseudo-label technology to obtain labeled data. Experiments on the ISIC18 show that the effect of the data annotated by our method for segmentation task is is equal to or even better than that of doctors annotations.
  arXiv  Detail & Related papers  (2022-04-22T05:54:14Z)
• FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis [55.939957482776194]
  We propose a new benchmark for federated domain translation on unsupervised brain image synthesis (termed as FedMed-GAN) FedMed-GAN mitigates the mode collapse without sacrificing the performance of generators. A comprehensive evaluation is provided for comparing FedMed-GAN and other centralized methods.
  arXiv  Detail & Related papers  (2022-01-22T02:50:29Z)
• Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity [77.78624443410216]
  We propose improved FDAM algorithms for detecting heterogeneous chest data. A result of this paper is that the communication of the proposed algorithm is strongly independent of the number of machines and also independent of the accuracy level. Experiments have demonstrated the effectiveness of our FDAM algorithm on benchmark datasets and on medical chest Xray images from different organizations.
  arXiv  Detail & Related papers  (2021-02-09T04:05:19Z)
• Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels [2.891413712995642]
  Unlabeled data is often abundant in the clinic, making machine learning methods based on semi-supervised learning a good match for this setting. Recently proposed MixMatch and FixMatch algorithms have demonstrated promising results in extracting useful representations. We find that both algorithms outperform the transfer learning baseline on all fractions of labelled data.
  arXiv  Detail & Related papers  (2020-10-23T11:47:28Z)
• 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)
• ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation [99.90263375737362]
  We propose ATSO, an asynchronous version of teacher-student optimization. ATSO partitions the unlabeled data into two subsets and alternately uses one subset to fine-tune the model and updates the label on the other subset. We evaluate ATSO on two popular medical image segmentation datasets and show its superior performance in various semi-supervised settings.
  arXiv  Detail & Related papers  (2020-06-24T04:05:12Z)
• Data Augmentation for Histopathological Images Based on Gaussian-Laplacian Pyramid Blending [59.91656519028334]
  Data imbalance is a major problem that affects several machine learning (ML) algorithms. In this paper, we propose a novel approach capable of not only augmenting HI dataset but also distributing the inter-patient variability. Experimental results on the BreakHis dataset have shown promising gains vis-a-vis the majority of DA techniques presented in the literature.
  arXiv  Detail & Related papers  (2020-01-31T22:02:57Z)

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.