114 research outputs found
HCDG: A Hierarchical Consistency Framework for Domain Generalization on Medical Image Segmentation
Modern deep neural networks struggle to transfer knowledge and generalize
across diverse domains when deployed to real-world applications. Currently,
domain generalization (DG) is introduced to learn a universal representation
from multiple domains to improve the network generalization ability on unseen
domains. However, previous DG methods only focus on the data-level consistency
scheme without considering the synergistic regularization among different
consistency schemes. In this paper, we present a novel Hierarchical Consistency
framework for Domain Generalization (HCDG) by integrating Extrinsic Consistency
and Intrinsic Consistency synergistically. Particularly, for the Extrinsic
Consistency, we leverage the knowledge across multiple source domains to
enforce data-level consistency. To better enhance such consistency, we design a
novel Amplitude Gaussian-mixing strategy into Fourier-based data augmentation
called DomainUp. For the Intrinsic Consistency, we perform task-level
consistency for the same instance under the dual-task scenario. We evaluate the
proposed HCDG framework on two medical image segmentation tasks, i.e., optic
cup/disc segmentation on fundus images and prostate MRI segmentation. Extensive
experimental results manifest the effectiveness and versatility of our HCDG
framework.Comment: this paper is currently not publishe
Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation
In the domain adaptation problem, source data may be unavailable to the
target client side due to privacy or intellectual property issues. Source-free
unsupervised domain adaptation (SF-UDA) aims at adapting a model trained on the
source side to align the target distribution with only the source model and
unlabeled target data. The source model usually produces noisy and
context-inconsistent pseudo-labels on the target domain, i.e., neighbouring
regions that have a similar visual appearance are annotated with different
pseudo-labels. This observation motivates us to refine pseudo-labels with
context relations. Another observation is that features of the same class tend
to form a cluster despite the domain gap, which implies context relations can
be readily calculated from feature distances. To this end, we propose a
context-aware pseudo-label refinement method for SF-UDA. Specifically, a
context-similarity learning module is developed to learn context relations.
Next, pseudo-label revision is designed utilizing the learned context
relations. Further, we propose calibrating the revised pseudo-labels to
compensate for wrong revision caused by inaccurate context relations.
Additionally, we adopt a pixel-level and class-level denoising scheme to select
reliable pseudo-labels for domain adaptation. Experiments on cross-domain
fundus images indicate that our approach yields the state-of-the-art results.
Code is available at https://github.com/xmed-lab/CPR.Comment: Accepted by MICCAI 2023, 11 page
Dual Consistency Enabled Weakly and Semi-Supervised Optic Disc and Cup Segmentation with Dual Adaptive Graph Convolutional Networks.
Glaucoma is a progressive eye disease that results in permanent vision loss, and the vertical cup to disc ratio (vCDR) in colour fundus images is essential in glaucoma screening and assessment. Previous fully supervised convolution neural networks segment the optic disc (OD) and optic cup (OC) from color fundus images and then calculate the vCDR offline. However, they rely on a large set of labeled masks for training, which is expensive and time-consuming to acquire. To address this, we propose a weakly and semi-supervised graph-based network that investigates geometric associations and domain knowledge between segmentation probability maps (PM), modified signed distance function representations (mSDF), and boundary region of interest characteristics (B-ROI) in three aspects. Firstly, we propose a novel Dual Adaptive Graph Convolutional Network (DAGCN) to reason the long-range features of the PM and the mSDF w.r.t. the regional uniformity. Secondly, we propose a dual consistency regularization-based semi-supervised learning paradigm. The regional consistency between the PM and the mSDF, and the marginal consistency between the derived B-ROI from each of them boost the proposed model's performance due to the inherent geometric associations. Thirdly, we exploit the task-specific domain knowledge via the oval shapes of OD & OC, where a differentiable vCDR estimating layer is proposed. Furthermore, without additional annotations, the supervision on vCDR serves as weakly-supervisions for segmentation tasks. Experiments on six large-scale datasets demonstrate our model's superior performance on OD & OC segmentation and vCDR estimation. The implementation code has been made available 1
Shape-Aware Weakly/Semi-Supervised Optic Disc and Cup Segmentation with Regional/Marginal Consistency
Glaucoma is a chronic eye disease that permanently impairs vision. Vertical cup to disc ratio (vCDR) is essential for glaucoma screening. Thus, accurately segmenting the optic disc (OD) and optic cup (OC) from colour fundus images is essential. Previous fully-supervised methods achieved accurate segmentation results; then, they calculated the vCDR with offline post-processing step. However, a large set of labeled segmentation images are required for the training, which is costly and time-consuming. To solve this, we propose a weakly/semi-supervised framework with the benefits of geometric associations and specific domain knowledge between pixel-wise segmentation probability map (PM), geometry-aware modified signed distance function representations (mSDF), and local boundary region of interest characteristics (B-ROI). Firstly, we propose a dual consistency regularisation based semi-supervised paradigm, where the regional and marginal consistency benefits the proposed model from the objects’ inherent region and boundary coherence of a large amount of unlabeled data. Secondly, for the first time, we exploit the domain-specific knowledge between the boundary and region in terms of the perimeter and area of an oval shape of OD & OC, where a differentiable vCDR estimating module is proposed for the end-to-end training. Thus, our model does not need any offline post-process to generate vCDR. Furthermore, without requiring any additional laborious annotations, the supervision on vCDR can serve as a weakly-supervision for OD & OC region and boundary segmentation. Experiments on six large-scale datasets demonstrate that our method outperforms state-of-the-art semi-supervised approaches for segmentation of the optic disc and optic cup, and estimation of vCDR for glaucoma assessment in colour fundus images, respectively. The implementation code is made available. (https://github.com/smallmax00/Share_aware_Weakly-Semi_ODOC_seg
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