Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis
- URL: http://arxiv.org/abs/2101.00318v2
- Date: Mon, 11 Jan 2021 15:09:03 GMT
- Title: Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis
- Authors: Xiaofeng Liu, Xiongchang Liu, Bo Hu, Wenxuan Ji, Fangxu Xing, Jun Lu,
Jane You, C.-C. Jay Kuo, Georges El Fakhri, Jonghye Woo
- Abstract summary: We propose to adaptively carry out the fine-grained subtype-aware alignment by explicitly enforcing the class-wise separation and subtype-wise compactness with intermediate pseudo labels.
Our key insight is that the unlabeled subtypes of a class can be divergent to one another with different conditional and label shifts.
The proposed subtype-aware dynamic UDA achieves promising results on medical diagnosis tasks.
- Score: 43.57869330587104
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advances in unsupervised domain adaptation (UDA) show that
transferable prototypical learning presents a powerful means for class
conditional alignment, which encourages the closeness of cross-domain class
centroids. However, the cross-domain inner-class compactness and the underlying
fine-grained subtype structure remained largely underexplored. In this work, we
propose to adaptively carry out the fine-grained subtype-aware alignment by
explicitly enforcing the class-wise separation and subtype-wise compactness
with intermediate pseudo labels. Our key insight is that the unlabeled subtypes
of a class can be divergent to one another with different conditional and label
shifts, while inheriting the local proximity within a subtype. The cases of
with or without the prior information on subtype numbers are investigated to
discover the underlying subtype structure in an online fashion. The proposed
subtype-aware dynamic UDA achieves promising results on medical diagnosis
tasks.
Related papers
- Crucial Semantic Classifier-based Adversarial Learning for Unsupervised
Domain Adaptation [4.6899218408452885]
Unsupervised Domain Adaptation (UDA) aims to explore the transferrable from a well-labeled source domain to a related unlabeled target domain.
We propose Crucial Semantic-based Adrial Learning (CSCAL) to pay more attention to crucial semantic knowledge transferring.
CSCAL can be effortlessly merged into different UDA methods as a regularizer and dramatically promote their performance.
arXiv Detail & Related papers (2023-02-03T13:06:14Z) - Subtype-Aware Dynamic Unsupervised Domain Adaptation [36.996764621968204]
Unsupervised domain adaptation (UDA) has been successfully applied to transfer knowledge from a labeled source domain to target domains without their labels.
We propose a new approach to adaptively perform a fine-grained subtype-aware alignment to improve performance in the target domain without the subtype label in both domains.
arXiv Detail & Related papers (2022-08-16T14:02:47Z) - Prototypical Contrast Adaptation for Domain Adaptive Semantic
Segmentation [52.63046674453461]
Prototypical Contrast Adaptation (ProCA) is a contrastive learning method for unsupervised domain adaptive semantic segmentation.
ProCA incorporates inter-class information into class-wise prototypes, and adopts the class-centered distribution alignment for adaptation.
arXiv Detail & Related papers (2022-07-14T04:54:26Z) - Domain Adaptive Nuclei Instance Segmentation and Classification via
Category-aware Feature Alignment and Pseudo-labelling [65.40672505658213]
We propose a novel deep neural network, namely Category-Aware feature alignment and Pseudo-Labelling Network (CAPL-Net) for UDA nuclei instance segmentation and classification.
Our approach outperforms state-of-the-art UDA methods with a remarkable margin.
arXiv Detail & Related papers (2022-07-04T07:05:06Z) - Geometry-Aware Unsupervised Domain Adaptation [12.298214579392129]
Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain in the presence of dataset shift.
Most existing methods cannot address the domain alignment and class discrimination well, which may distort the intrinsic data structure for downstream tasks.
We propose a novel geometry-aware model to learn the transferability and discriminability simultaneously via nuclear norm optimization.
arXiv Detail & Related papers (2021-12-21T08:45:42Z) - Simultaneous Semantic Alignment Network for Heterogeneous Domain
Adaptation [67.37606333193357]
We propose aSimultaneous Semantic Alignment Network (SSAN) to simultaneously exploit correlations among categories and align the centroids for each category across domains.
By leveraging target pseudo-labels, a robust triplet-centroid alignment mechanism is explicitly applied to align feature representations for each category.
Experiments on various HDA tasks across text-to-image, image-to-image and text-to-text successfully validate the superiority of our SSAN against state-of-the-art HDA methods.
arXiv Detail & Related papers (2020-08-04T16:20:37Z) - Domain Adaptive Semantic Segmentation Using Weak Labels [115.16029641181669]
We propose a novel framework for domain adaptation in semantic segmentation with image-level weak labels in the target domain.
We develop a weak-label classification module to enforce the network to attend to certain categories.
In experiments, we show considerable improvements with respect to the existing state-of-the-arts in UDA and present a new benchmark in the WDA setting.
arXiv Detail & Related papers (2020-07-30T01:33:57Z) - Domain Adaptation with Auxiliary Target Domain-Oriented Classifier [115.39091109079622]
Domain adaptation aims to transfer knowledge from a label-rich but heterogeneous domain to a label-scare domain.
One of the most popular SSL techniques is pseudo-labeling that assigns pseudo labels for each unlabeled data.
We propose a new pseudo-labeling framework called Auxiliary Target Domain-Oriented (ATDOC)
ATDOC alleviates the bias by introducing an auxiliary classifier for target data only, to improve the quality of pseudo labels.
arXiv Detail & Related papers (2020-07-08T15:01:35Z)
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.