1,529 research outputs found
MR Acquisition-Invariant Representation Learning
Voxelwise classification approaches are popular and effective methods for
tissue quantification in brain magnetic resonance imaging (MRI) scans. However,
generalization of these approaches is hampered by large differences between
sets of MRI scans such as differences in field strength, vendor or acquisition
protocols. Due to this acquisition related variation, classifiers trained on
data from a specific scanner fail or under-perform when applied to data that
was acquired differently. In order to address this lack of generalization, we
propose a Siamese neural network (MRAI-net) to learn a representation that
minimizes the between-scanner variation, while maintaining the contrast between
brain tissues necessary for brain tissue quantification. The proposed MRAI-net
was evaluated on both simulated and real MRI data. After learning the MR
acquisition invariant representation, any supervised classification model that
uses feature vectors can be applied. In this paper, we provide a proof of
principle, which shows that a linear classifier applied on the MRAI
representation is able to outperform supervised convolutional neural network
classifiers for tissue classification when little target training data is
available.Comment: 36 pages, 2 appendices, 12 figures, 3 table
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Machine learning (ML) algorithms have made a tremendous impact in the field
of medical imaging. While medical imaging datasets have been growing in size, a
challenge for supervised ML algorithms that is frequently mentioned is the lack
of annotated data. As a result, various methods which can learn with less/other
types of supervision, have been proposed. We review semi-supervised, multiple
instance, and transfer learning in medical imaging, both in diagnosis/detection
or segmentation tasks. We also discuss connections between these learning
scenarios, and opportunities for future research.Comment: Submitted to Medical Image Analysi
Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review
Transfer learning refers to machine learning techniques that focus on
acquiring knowledge from related tasks to improve generalization in the tasks
of interest. In MRI, transfer learning is important for developing strategies
that address the variation in MR images. Additionally, transfer learning is
beneficial to re-utilize machine learning models that were trained to solve
related tasks to the task of interest. Our goal is to identify research
directions, gaps of knowledge, applications, and widely used strategies among
the transfer learning approaches applied in MR brain imaging. We performed a
systematic literature search for articles that applied transfer learning to MR
brain imaging. We screened 433 studies and we categorized and extracted
relevant information, including task type, application, and machine learning
methods. Furthermore, we closely examined brain MRI-specific transfer learning
approaches and other methods that tackled privacy, unseen target domains, and
unlabeled data. We found 129 articles that applied transfer learning to brain
MRI tasks. The most frequent applications were dementia related classification
tasks and brain tumor segmentation. A majority of articles utilized transfer
learning on convolutional neural networks (CNNs). Only few approaches were
clearly brain MRI specific, considered privacy issues, unseen target domains or
unlabeled data. We proposed a new categorization to group specific, widely-used
approaches. There is an increasing interest in transfer learning within brain
MRI. Public datasets have contributed to the popularity of Alzheimer's
diagnostics/prognostics and tumor segmentation. Likewise, the availability of
pretrained CNNs has promoted their utilization. Finally, the majority of the
surveyed studies did not examine in detail the interpretation of their
strategies after applying transfer learning, and did not compare to other
approaches.Comment: Accepted in Journal of Imagin
Self domain adapted network
Domain shift is a major problem for deploying deep networks in clinical
practice. Network performance drops significantly with (target) images obtained
differently than its (source) training data. Due to a lack of target label
data, most work has focused on unsupervised domain adaptation (UDA). Current
UDA methods need both source and target data to train models which perform
image translation (harmonization) or learn domain-invariant features. However,
training a model for each target domain is time consuming and computationally
expensive, even infeasible when target domain data are scarce or source data
are unavailable due to data privacy. In this paper, we propose a novel self
domain adapted network (SDA-Net) that can rapidly adapt itself to a single test
subject at the testing stage, without using extra data or training a UDA model.
The SDA-Net consists of three parts: adaptors, task model, and auto-encoders.
The latter two are pre-trained offline on labeled source images. The task model
performs tasks like synthesis, segmentation, or classification, which may
suffer from the domain shift problem. At the testing stage, the adaptors are
trained to transform the input test image and features to reduce the domain
shift as measured by the auto-encoders, and thus perform domain adaptation. We
validated our method on retinal layer segmentation from different OCT scanners
and T1 to T2 synthesis with T1 from different MRI scanners and with different
imaging parameters. Results show that our SDA-Net, with a single test subject
and a short amount of time for self adaptation at the testing stage, can
achieve significant improvements.Comment: early accept in miccai202
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
Transfer learning by feature-space transformation: A method for Hippocampus segmentation across scanners
Many successful approaches in MR brain segmentation use supervised voxel classification, which requires manually labeled training images that are representative of the test images to segment. However, the performance of such methods often deteriorates if training and test images are acquired with different scanners or scanning parameters, since this leads to differences in feature representations between training and test data. In this paper we propose a feature-space transformation (FST) to overcome such differences in feature representations. The proposed FST is derived from unlabeled images of a subject that was scanned with both the source and the target scan protocol. After an affine registration, these images give a mapping between source and target voxels in the feature space. This mapping is then used to map all training samples to the feature representation of the test samples. We evaluated the benefit of the proposed FST on hippocampus segmentation. Experiments were performed on two datasets: one with relatively small differences between training and test images and one with large differences. In both cases, the FST significantly improved the performance compared to using only image normalization. Additionally, we showed that our FST can be used to improve the performance of a state-of-the-art patch-based-atlas-fusion technique in case of large differences between scanners
Domain Adaptive Medical Image Segmentation via Adversarial Learning of Disease-Specific Spatial Patterns
In medical imaging, the heterogeneity of multi-centre data impedes the
applicability of deep learning-based methods and results in significant
performance degradation when applying models in an unseen data domain, e.g. a
new centreor a new scanner. In this paper, we propose an unsupervised domain
adaptation framework for boosting image segmentation performance across
multiple domains without using any manual annotations from the new target
domains, but by re-calibrating the networks on few images from the target
domain. To achieve this, we enforce architectures to be adaptive to new data by
rejecting improbable segmentation patterns and implicitly learning through
semantic and boundary information, thus to capture disease-specific spatial
patterns in an adversarial optimization. The adaptation process needs
continuous monitoring, however, as we cannot assume the presence of
ground-truth masks for the target domain, we propose two new metrics to monitor
the adaptation process, and strategies to train the segmentation algorithm in a
stable fashion. We build upon well-established 2D and 3D architectures and
perform extensive experiments on three cross-centre brain lesion segmentation
tasks, involving multicentre public and in-house datasets. We demonstrate that
recalibrating the deep networks on a few unlabeled images from the target
domain improves the segmentation accuracy significantly.Comment: submitted to a journal and under revie
Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency
In this paper, we introduce a novel unsupervised domain adaptation technique
for the task of 3D keypoint prediction from a single depth scan or image. Our
key idea is to utilize the fact that predictions from different views of the
same or similar objects should be consistent with each other. Such view
consistency can provide effective regularization for keypoint prediction on
unlabeled instances. In addition, we introduce a geometric alignment term to
regularize predictions in the target domain. The resulting loss function can be
effectively optimized via alternating minimization. We demonstrate the
effectiveness of our approach on real datasets and present experimental results
showing that our approach is superior to state-of-the-art general-purpose
domain adaptation techniques.Comment: ECCV 201
Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image Segmentation
Accurate and robust medical image segmentation is fundamental and crucial for
enhancing the autonomy of computer-aided diagnosis and intervention systems.
Medical data collection normally involves different scanners, protocols, and
populations, making domain adaptation (DA) a highly demanding research field to
alleviate model degradation in the deployment site. To preserve the model
performance across multiple testing domains, this work proposes the
Curriculum-based Augmented Fourier Domain Adaptation (Curri-AFDA) for robust
medical image segmentation. In particular, our curriculum learning strategy is
based on the causal relationship of a model under different levels of data
shift in the deployment phase, where the higher the shift is, the harder to
recognize the variance. Considering this, we progressively introduce more
amplitude information from the target domain to the source domain in the
frequency space during the curriculum-style training to smoothly schedule the
semantic knowledge transfer in an easier-to-harder manner. Besides, we
incorporate the training-time chained augmentation mixing to help expand the
data distributions while preserving the domain-invariant semantics, which is
beneficial for the acquired model to be more robust and generalize better to
unseen domains. Extensive experiments on two segmentation tasks of Retina and
Nuclei collected from multiple sites and scanners suggest that our proposed
method yields superior adaptation and generalization performance. Meanwhile,
our approach proves to be more robust under various corruption types and
increasing severity levels. In addition, we show our method is also beneficial
in the domain-adaptive classification task with skin lesion datasets. The code
is available at https://github.com/lofrienger/Curri-AFDA.Comment: Work under review. First three authors contributed equall
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