UOD: Universal One-shot Detection of Anatomical Landmarks

• URL: http://arxiv.org/abs/2306.07615v5
• Date: Tue, 18 Jul 2023 02:15:43 GMT
• Title: UOD: Universal One-shot Detection of Anatomical Landmarks
• Authors: Heqin Zhu, Quan Quan, Qingsong Yao, Zaiyi Liu, S. Kevin Zhou
• Abstract summary: We develop a domain-adaptive one-shot landmark detection framework for handling multi-domain medical images, named Universal One-shot Detection (UOD) UOD consists of two stages and two corresponding universal models which are designed as combinations of domain-specific modules and domain-shared modules. We investigate both qualitatively and quantitatively the proposed UOD on three widely-used public X-ray datasets in different anatomical domains.
• Score: 16.360644135635333
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: One-shot medical landmark detection gains much attention and achieves great success for its label-efficient training process. However, existing one-shot learning methods are highly specialized in a single domain and suffer domain preference heavily in the situation of multi-domain unlabeled data. Moreover, one-shot learning is not robust that it faces performance drop when annotating a sub-optimal image. To tackle these issues, we resort to developing a domain-adaptive one-shot landmark detection framework for handling multi-domain medical images, named Universal One-shot Detection (UOD). UOD consists of two stages and two corresponding universal models which are designed as combinations of domain-specific modules and domain-shared modules. In the first stage, a domain-adaptive convolution model is self-supervised learned to generate pseudo landmark labels. In the second stage, we design a domain-adaptive transformer to eliminate domain preference and build the global context for multi-domain data. Even though only one annotated sample from each domain is available for training, the domain-shared modules help UOD aggregate all one-shot samples to detect more robust and accurate landmarks. We investigated both qualitatively and quantitatively the proposed UOD on three widely-used public X-ray datasets in different anatomical domains (i.e., head, hand, chest) and obtained state-of-the-art performances in each domain. The code is available at https://github.com/heqin-zhu/UOD_universal_oneshot_detection.

Related papers

• Can Out-of-Domain data help to Learn Domain-Specific Prompts for Multimodal Misinformation Detection? [14.722270908687216]
  Domain-specific Prompt tuning can exploit out-of-domain data during training to improve fake news detection of all desired domains simultaneously. Experiments on the large-scale NewsCLIPpings and VERITE benchmarks demonstrate that DPOD achieves state-the-art performance for this challenging task.
  arXiv  Detail & Related papers  (2023-11-27T08:49:26Z)
• Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation [72.70876977882882]
  Domain shift is a common problem in clinical applications, where the training images (source domain) and the test images (target domain) are under different distributions. We propose a novel method for Few-Shot Unsupervised Domain Adaptation (FSUDA), where only a limited number of unlabeled target domain samples are available for training.
  arXiv  Detail & Related papers  (2023-09-03T16:02:01Z)
• Unsupervised Domain Adaptation for Anatomical Landmark Detection [5.070344284426738]
  We propose a novel framework for anatomical landmark detection under the setting of unsupervised domain adaptation (UDA) The framework leverages self-training and domain adversarial learning to address the domain gap during adaptation. Our experiments on cephalometric and lung landmark detection show the effectiveness of the method, which reduces the domain gap by a large margin and outperforms other UDA methods consistently.
  arXiv  Detail & Related papers  (2023-08-25T10:22:13Z)
• Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis [68.742820522137]
  Cross-domain aspect-based sentiment analysis (ABSA) aims to perform various fine-grained sentiment analysis tasks on a target domain by transferring knowledge from a source domain. We propose a unified bidirectional generative framework to tackle various cross-domain ABSA tasks. Our framework trains a generative model in both text-to-label and label-to-text directions.
  arXiv  Detail & Related papers  (2023-05-16T15:02:23Z)
• MultiMatch: Multi-task Learning for Semi-supervised Domain Generalization [55.06956781674986]
  We resort to solving the semi-supervised domain generalization task, where there are a few label information in each source domain. We propose MultiMatch, extending FixMatch to the multi-task learning framework, producing the high-quality pseudo-label for SSDG. A series of experiments validate the effectiveness of the proposed method, and it outperforms the existing semi-supervised methods and the SSDG method on several benchmark DG datasets.
  arXiv  Detail & Related papers  (2022-08-11T14:44:33Z)
• Adaptive Domain Generalization via Online Disagreement Minimization [17.215683606365445]
  Domain Generalization aims to safely transfer a model to unseen target domains. AdaODM adaptively modifies the source model at test time for different target domains. Results show AdaODM stably improves the generalization capacity on unseen domains.
  arXiv  Detail & Related papers  (2022-08-03T11:51:11Z)
• Dynamic Instance Domain Adaptation [109.53575039217094]
  Most studies on unsupervised domain adaptation assume that each domain's training samples come with domain labels. We develop a dynamic neural network with adaptive convolutional kernels to generate instance-adaptive residuals to adapt domain-agnostic deep features to each individual instance. Our model, dubbed DIDA-Net, achieves state-of-the-art performance on several commonly used single-source and multi-source UDA datasets.
  arXiv  Detail & Related papers  (2022-03-09T20:05:54Z)
• Cross-domain Contrastive Learning for Unsupervised Domain Adaptation [108.63914324182984]
  Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source domain to a different unlabeled target domain. We build upon contrastive self-supervised learning to align features so as to reduce the domain discrepancy between training and testing sets.
  arXiv  Detail & Related papers  (2021-06-10T06:32:30Z)
• 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)

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.