C-MADA: Unsupervised Cross-Modality Adversarial Domain Adaptation framework for medical Image Segmentation

• URL: http://arxiv.org/abs/2110.15823v1
• Date: Fri, 29 Oct 2021 14:34:33 GMT
• Title: C-MADA: Unsupervised Cross-Modality Adversarial Domain Adaptation framework for medical Image Segmentation
• Authors: Maria Baldeon-Calisto, Susana K. Lai-Yuen
• Abstract summary: We present an unsupervised Cross-Modality Adversarial Domain Adaptation (C-MADA) framework for medical image segmentation. C-MADA implements an image- and feature-level adaptation method in a sequential manner. It is tested on the task of brain MRI segmentation, obtaining competitive results.
• Score: 0.8680676599607122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning models have obtained state-of-the-art results for medical image analysis. However, when these models are tested on an unseen domain there is a significant performance degradation. In this work, we present an unsupervised Cross-Modality Adversarial Domain Adaptation (C-MADA) framework for medical image segmentation. C-MADA implements an image- and feature-level adaptation method in a sequential manner. First, images from the source domain are translated to the target domain through an un-paired image-to-image adversarial translation with cycle-consistency loss. Then, a U-Net network is trained with the mapped source domain images and target domain images in an adversarial manner to learn domain-invariant feature representations. Furthermore, to improve the networks segmentation performance, information about the shape, texture, and con-tour of the predicted segmentation is included during the adversarial train-ing. C-MADA is tested on the task of brain MRI segmentation, obtaining competitive results.

Related papers

• SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation [100.86339246424541]
  We propose a novel Structure-Modal Constrained (SMC) UDA framework based on a discriminative paradigm and introduce edge structure as a bridge between domains. With the structure-constrained self-learning and progressive ROI, our methods segment the kidney by locating the 3D spatial structure of the edge. experiments show that our proposed SMC-UDA has a strong generalization and outperforms generative UDA methods.
  arXiv  Detail & Related papers  (2023-06-14T02:57:23Z)
• Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation [84.58210297703714]
  We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting. Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
  arXiv  Detail & Related papers  (2023-01-12T08:19:46Z)
• Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures [21.54339967787734]
  We propose Contrastive Semi-supervised learning for Cross Anatomy Domain Adaptation. It adapts a model to segment similar structures in a target domain. It requires only limited annotations in the target domain.
  arXiv  Detail & Related papers  (2022-08-18T02:54:04Z)
• Causality-inspired Single-source Domain Generalization for Medical Image Segmentation [12.697945585457441]
  We propose a simple data augmentation approach to expose a segmentation model to synthesized domain-shifted training examples. Specifically, 1) to make the deep model robust to discrepancies in image intensities and textures, we employ a family of randomly-weighted shallow networks. We remove spurious correlations among objects in an image that might be taken by the network as domain-specific clues for making predictions, and they may break on unseen domains.
  arXiv  Detail & Related papers  (2021-11-24T14:45:17Z)
• Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation [61.01704175938995]
  In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA scheme. Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor. The proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin.
  arXiv  Detail & Related papers  (2021-05-17T10:11:45Z)
• Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation [9.659642285903418]
  Cross-modality synthesis of medical images to reduce the costly annotation burden by radiologists. We present a novel approach for image-to-image translation in medical images, capable of supervised or unsupervised (unpaired image data) setups.
  arXiv  Detail & Related papers  (2021-03-05T16:22:31Z)
• ICMSC: Intra- and Cross-modality Semantic Consistency for Unsupervised Domain Adaptation on Hip Joint Bone Segmentation [1.4148874598036136]
  We propose intra- and cross-modality semantic consistency (ICMSC) for UDA. Our proposed method achieves an average DICE of 81.61% on the acetabulum and 88.16% on the proximal femur. Without UDA, a model trained on CT for hip joint bone segmentation is non-transferable to MRI and has almost zero-DICE segmentation.
  arXiv  Detail & Related papers  (2020-12-23T09:58:38Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation [73.84166499988443]
  We present a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA) Our proposed SIFA conducts synergistic alignment of domains from both image and feature perspectives. Experimental results on two different tasks demonstrate that our SIFA method is effective in improving segmentation performance on unlabeled target images.
  arXiv  Detail & Related papers  (2020-02-06T13:49:47Z)
• CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency [119.45667331836583]
  Unsupervised domain adaptation algorithms aim to transfer the knowledge learned from one domain to another. We present a novel pixel-wise adversarial domain adaptation algorithm.
  arXiv  Detail & Related papers  (2020-01-09T19:00:35Z)

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.