Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification

• URL: http://arxiv.org/abs/2303.01283v1
• Date: Thu, 2 Mar 2023 14:07:36 GMT
• Title: Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification
• Authors: Shota Harada, Ryoma Bise, Kengo Araki, Akihiko Yoshizawa, Kazuhiro Terada, Mariyo Kurata, Naoki Nakajima, Hiroyuki Abe, Tetsuo Ushiku, Seiichi Uchida
• Abstract summary: We develop a semi-supervised domain adaptation method, which has robustness to class-imbalanced situations. For robustness, we propose a weakly-supervised clustering pipeline to obtain high-purity clusters. The proposed method showed state-of-the-art performance in the experiment using severely class-imbalanced pathological image patches.
• Score: 10.92984910426756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semi-supervised domain adaptation is a technique to build a classifier for a target domain by modifying a classifier in another (source) domain using many unlabeled samples and a small number of labeled samples from the target domain. In this paper, we develop a semi-supervised domain adaptation method, which has robustness to class-imbalanced situations, which are common in medical image classification tasks. For robustness, we propose a weakly-supervised clustering pipeline to obtain high-purity clusters and utilize the clusters in representation learning for domain adaptation. The proposed method showed state-of-the-art performance in the experiment using severely class-imbalanced pathological image patches.

Related papers

• Few-Shot Domain Adaptive Object Detection for Microscopic Images [7.993453987882035]
  Few-shot domain adaptive object detection (FSDAOD) addresses the challenge of adapting object detectors to target domains with limited labeled data. Medical datasets exhibit high class imbalance and background similarity, leading to increased false positives and lower mean Average Precision (map) in target domains. Our contributions include a domain adaptive class balancing strategy for few-shot scenarios, multi-layer instance-level inter and intra-domain alignment, and an instance-level classification loss.
  arXiv  Detail & Related papers  (2024-07-10T13:11:58Z)
• Polycentric Clustering and Structural Regularization for Source-free Unsupervised Domain Adaptation [20.952542421577487]
  Source-Free Domain Adaptation (SFDA) aims to solve the domain adaptation problem by transferring the knowledge learned from a pre-trained source model to an unseen target domain. Most existing methods assign pseudo-labels to the target data by generating feature prototypes. In this paper, a novel framework named PCSR is proposed to tackle SFDA via a novel intra-class Polycentric Clustering and Structural Regularization strategy.
  arXiv  Detail & Related papers  (2022-10-14T02:20:48Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)
• Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation [85.6961770631173]
  In semi-supervised domain adaptation, a few labeled samples per class in the target domain guide features of the remaining target samples to aggregate around them. We propose a novel approach called Cross-domain Adaptive Clustering to address this problem.
  arXiv  Detail & Related papers  (2021-04-19T16:07:32Z)
• Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation [74.71931918541748]
  We propose an instance affinity based criterion for source to target transfer during adaptation, called ILA-DA. We first propose a reliable and efficient method to extract similar and dissimilar samples across source and target, and utilize a multi-sample contrastive loss to drive the domain alignment process. We verify the effectiveness of ILA-DA by observing consistent improvements in accuracy over popular domain adaptation approaches on a variety of benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-03T01:33:14Z)
• 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)
• Learning a Domain Classifier Bank for Unsupervised Adaptive Object Detection [48.19258721979389]
  In this paper, we propose a fine-grained domain alignment approach for object detectors based on deep networks. We develop a bare object detector with the proposed fine-grained domain alignment mechanism as the adaptive detector. Experiments on three popular transferring benchmarks demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2020-07-06T09:12:46Z)
• 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)

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.