103 research outputs found
Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation
Accurate segmentation of the optic disc (OD) and cup (OC)in fundus images
from different datasets is critical for glaucoma disease screening. The
cross-domain discrepancy (domain shift) hinders the generalization of deep
neural networks to work on different domain datasets.In this work, we present
an unsupervised domain adaptation framework,called Boundary and Entropy-driven
Adversarial Learning (BEAL), to improve the OD and OC segmentation performance,
especially on the ambiguous boundary regions. In particular, our proposed BEAL
frame-work utilizes the adversarial learning to encourage the boundary
prediction and mask probability entropy map (uncertainty map) of the target
domain to be similar to the source ones, generating more accurate boundaries
and suppressing the high uncertainty predictions of OD and OC segmentation. We
evaluate the proposed BEAL framework on two public retinal fundus image
datasets (Drishti-GS and RIM-ONE-r3), and the experiment results demonstrate
that our method outperforms the state-of-the-art unsupervised domain adaptation
methods. Codes will be available at https://github.com/EmmaW8/BEAL.Comment: Accepted at MICCAI 201
Unsupervised Domain Adaptive Fundus Image Segmentation with Few Labeled Source Data
Deep learning-based segmentation methods have been widely employed for
automatic glaucoma diagnosis and prognosis. In practice, fundus images obtained
by different fundus cameras vary significantly in terms of illumination and
intensity. Although recent unsupervised domain adaptation (UDA) methods enhance
the models' generalization ability on the unlabeled target fundus datasets,
they always require sufficient labeled data from the source domain, bringing
auxiliary data acquisition and annotation costs. To further facilitate the data
efficiency of the cross-domain segmentation methods on the fundus images, we
explore UDA optic disc and cup segmentation problems using few labeled source
data in this work. We first design a Searching-based Multi-style Invariant
Mechanism to diversify the source data style as well as increase the data
amount. Next, a prototype consistency mechanism on the foreground objects is
proposed to facilitate the feature alignment for each kind of tissue under
different image styles. Moreover, a cross-style self-supervised learning stage
is further designed to improve the segmentation performance on the target
images. Our method has outperformed several state-of-the-art UDA segmentation
methods under the UDA fundus segmentation with few labeled source data.Comment: Accepted by The 33rd British Machine Vision Conference (BMVC) 202
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in
medical imaging. However, these approaches primarily focus on supervised
learning, assuming that the training and testing data are drawn from the same
distribution. Unfortunately, this assumption may not always hold true in
practice. To address these issues, unsupervised domain adaptation (UDA)
techniques have been developed to transfer knowledge from a labeled domain to a
related but unlabeled domain. In recent years, significant advancements have
been made in UDA, resulting in a wide range of methodologies, including feature
alignment, image translation, self-supervision, and disentangled representation
methods, among others. In this paper, we provide a comprehensive literature
review of recent deep UDA approaches in medical imaging from a technical
perspective. Specifically, we categorize current UDA research in medical
imaging into six groups and further divide them into finer subcategories based
on the different tasks they perform. We also discuss the respective datasets
used in the studies to assess the divergence between the different domains.
Finally, we discuss emerging areas and provide insights and discussions on
future research directions to conclude this survey.Comment: Under Revie
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