Cluster Entropy: Active Domain Adaptation in Pathological Image Segmentation

• URL: http://arxiv.org/abs/2304.13513v1
• Date: Wed, 26 Apr 2023 12:53:41 GMT
• Title: Cluster Entropy: Active Domain Adaptation in Pathological Image Segmentation
• Authors: Xiaoqing Liu, Kengo Araki, Shota Harada, Akihiko Yoshizawa, Kazuhiro Terada, Mariyo Kurata, Naoki Nakajima, Hiroyuki Abe, Tetsuo Ushiku, Ryoma Bise
• Abstract summary: We propose a cluster entropy for selecting an effective whole slide image (WSI) that is used for semi-supervised domain adaptation. This approach can measure how the image features of the WSI cover the entire distribution of the target domain. Our approach achieved competitive results against the prior arts on datasets collected from two hospitals.
• Score: 7.500364675640502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The domain shift in pathological segmentation is an important problem, where a network trained by a source domain (collected at a specific hospital) does not work well in the target domain (from different hospitals) due to the different image features. Due to the problems of class imbalance and different class prior of pathology, typical unsupervised domain adaptation methods do not work well by aligning the distribution of source domain and target domain. In this paper, we propose a cluster entropy for selecting an effective whole slide image (WSI) that is used for semi-supervised domain adaptation. This approach can measure how the image features of the WSI cover the entire distribution of the target domain by calculating the entropy of each cluster and can significantly improve the performance of domain adaptation. Our approach achieved competitive results against the prior arts on datasets collected from two hospitals.

Related papers

• I2F: A Unified Image-to-Feature Approach for Domain Adaptive Semantic Segmentation [55.633859439375044]
  Unsupervised domain adaptation (UDA) for semantic segmentation is a promising task freeing people from heavy annotation work. Key idea to tackle this problem is to perform both image-level and feature-level adaptation jointly. This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation.
  arXiv  Detail & Related papers  (2023-01-03T15:19:48Z)
• Contrastive Domain Disentanglement for Generalizable Medical Image Segmentation [12.863227646939563]
  We propose Contrastive Disentangle Domain (CDD) network for generalizable medical image segmentation. We first introduce a disentangle network to decompose medical images into an anatomical representation factor and a modality representation factor. We then propose a domain augmentation strategy that can randomly generate new domains for model generalization training.
  arXiv  Detail & Related papers  (2022-05-13T10:32:41Z)
• From Big to Small: Adaptive Learning to Partial-Set Domains [94.92635970450578]
  Domain adaptation targets at knowledge acquisition and dissemination from a labeled source domain to an unlabeled target domain under distribution shift. Recent advances show that deep pre-trained models of large scale endow rich knowledge to tackle diverse downstream tasks of small scale. This paper introduces Partial Domain Adaptation (PDA), a learning paradigm that relaxes the identical class space assumption to that the source class space subsumes the target class space.
  arXiv  Detail & Related papers  (2022-03-14T07:02:45Z)
• Unsupervised Domain Adaptation for Retinal Vessel Segmentation with Adversarial Learning and Transfer Normalization [22.186070895966022]
  We propose an entropy-based adversarial learning strategy to reduce the distribution discrepancy between source and target domains. A new transfer normalization layer is proposed to further boost the transferability of the deep network. Our approach yields significant performance gains compared to other state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-08-04T02:45:37Z)
• Unsupervised Domain Adaptation with Variational Approximation for Cardiac Segmentation [15.2292571922932]
  Unsupervised domain adaptation is useful in medical image segmentation. We propose a new framework, where the latent features of both domains are driven towards a common and parameterized variational form. This is achieved by two networks based on variational auto-encoders (VAEs) and a regularization for this variational approximation.
  arXiv  Detail & Related papers  (2021-06-16T13:00:39Z)
• Unsupervised Domain Adaptation Network with Category-Centric Prototype Aligner for Biomedical Image Segmentation [1.1799563040751586]
  We present a novel unsupervised domain adaptation network for generalizing models learned from the labeled source domain to the unlabeled target domain. Specifically, our approach consists of two key modules, a conditional domain discriminator(CDD) and a category-centric prototype aligner(CCPA)
  arXiv  Detail & Related papers  (2021-03-03T07:07:38Z)
• Cross-Domain Grouping and Alignment for Domain Adaptive Semantic Segmentation [74.3349233035632]
  Existing techniques to adapt semantic segmentation networks across the source and target domains within deep convolutional neural networks (CNNs) do not consider an inter-class variation within the target domain itself or estimated category. We introduce a learnable clustering module, and a novel domain adaptation framework called cross-domain grouping and alignment. Our method consistently boosts the adaptation performance in semantic segmentation, outperforming the state-of-the-arts on various domain adaptation settings.
  arXiv  Detail & Related papers  (2020-12-15T11:36:21Z)
• Class Distribution Alignment for Adversarial Domain Adaptation [32.95056492475652]
  Conditional ADversarial Image Translation (CADIT) is proposed to explicitly align the class distributions given samples between the two domains. It integrates a discriminative structure-preserving loss and a joint adversarial generation loss. Our approach achieves superior classification in the target domain when compared to the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-04-20T15:58:11Z)
• Towards Fair Cross-Domain Adaptation via Generative Learning [50.76694500782927]
  Domain Adaptation (DA) targets at adapting a model trained over the well-labeled source domain to the unlabeled target domain lying in different distributions. We develop a novel Generative Few-shot Cross-domain Adaptation (GFCA) algorithm for fair cross-domain classification.
  arXiv  Detail & Related papers  (2020-03-04T23:25:09Z)
• 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.