9,960 research outputs found
Source-Relaxed Domain Adaptation for Image Segmentation
Domain adaptation (DA) has drawn high interests for its capacity to adapt a
model trained on labeled source data to perform well on unlabeled or weakly
labeled target data from a different domain. Most common DA techniques require
the concurrent access to the input images of both the source and target
domains. However, in practice, it is common that the source images are not
available in the adaptation phase. This is a very frequent DA scenario in
medical imaging, for instance, when the source and target images come from
different clinical sites. We propose a novel formulation for adapting
segmentation networks, which relaxes such a constraint. Our formulation is
based on minimizing a label-free entropy loss defined over target-domain data,
which we further guide with a domain invariant prior on the segmentation
regions. Many priors can be used, derived from anatomical information. Here, a
class-ratio prior is learned via an auxiliary network and integrated in the
form of a Kullback-Leibler (KL) divergence in our overall loss function. We
show the effectiveness of our prior-aware entropy minimization in adapting
spine segmentation across different MRI modalities. Our method yields
comparable results to several state-of-the-art adaptation techniques, even
though is has access to less information, the source images being absent in the
adaptation phase. Our straight-forward adaptation strategy only uses one
network, contrary to popular adversarial techniques, which cannot perform
without the presence of the source images. Our framework can be readily used
with various priors and segmentation problems
Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation
Unsupervised domain adaptation (UDA) has been a vital protocol for migrating
information learned from a labeled source domain to facilitate the
implementation in an unlabeled heterogeneous target domain. Although UDA is
typically jointly trained on data from both domains, accessing the labeled
source domain data is often restricted, due to concerns over patient data
privacy or intellectual property. To sidestep this, we propose "off-the-shelf
(OS)" UDA (OSUDA), aimed at image segmentation, by adapting an OS segmentor
trained in a source domain to a target domain, in the absence of source domain
data in adaptation. Toward this goal, we aim to develop a novel batch-wise
normalization (BN) statistics adaptation framework. In particular, we gradually
adapt the domain-specific low-order BN statistics, e.g., mean and variance,
through an exponential momentum decay strategy, while explicitly enforcing the
consistency of the domain shareable high-order BN statistics, e.g., scaling and
shifting factors, via our optimization objective. We also adaptively quantify
the channel-wise transferability to gauge the importance of each channel, via
both low-order statistics divergence and a scaling factor.~Furthermore, we
incorporate unsupervised self-entropy minimization into our framework to boost
performance alongside a novel queued, memory-consistent self-training strategy
to utilize the reliable pseudo label for stable and efficient unsupervised
adaptation. We evaluated our OSUDA-based framework on both cross-modality and
cross-subtype brain tumor segmentation and cardiac MR to CT segmentation tasks.
Our experimental results showed that our memory consistent OSUDA performs
better than existing source-relaxed UDA methods and yields similar performance
to UDA methods with source data.Comment: Published in Medical Image Analysis (extension of MICCAI paper
Dynamic Adaptation on Non-Stationary Visual Domains
Domain adaptation aims to learn models on a supervised source domain that
perform well on an unsupervised target. Prior work has examined domain
adaptation in the context of stationary domain shifts, i.e. static data sets.
However, with large-scale or dynamic data sources, data from a defined domain
is not usually available all at once. For instance, in a streaming data
scenario, dataset statistics effectively become a function of time. We
introduce a framework for adaptation over non-stationary distribution shifts
applicable to large-scale and streaming data scenarios. The model is adapted
sequentially over incoming unsupervised streaming data batches. This enables
improvements over several batches without the need for any additionally
annotated data. To demonstrate the effectiveness of our proposed framework, we
modify associative domain adaptation to work well on source and target data
batches with unequal class distributions. We apply our method to several
adaptation benchmark datasets for classification and show improved classifier
accuracy not only for the currently adapted batch, but also when applied on
future stream batches. Furthermore, we show the applicability of our
associative learning modifications to semantic segmentation, where we achieve
competitive results
A review of domain adaptation without target labels
Domain adaptation has become a prominent problem setting in machine learning
and related fields. This review asks the question: how can a classifier learn
from a source domain and generalize to a target domain? We present a
categorization of approaches, divided into, what we refer to as, sample-based,
feature-based and inference-based methods. Sample-based methods focus on
weighting individual observations during training based on their importance to
the target domain. Feature-based methods revolve around on mapping, projecting
and representing features such that a source classifier performs well on the
target domain and inference-based methods incorporate adaptation into the
parameter estimation procedure, for instance through constraints on the
optimization procedure. Additionally, we review a number of conditions that
allow for formulating bounds on the cross-domain generalization error. Our
categorization highlights recurring ideas and raises questions important to
further research.Comment: 20 pages, 5 figure
MIRACLE Retrieval Experiments with East Asian Languages
This paper describes the participation of MIRACLE in NTCIR 2005 CLIR task. Although our group has a strong background and long expertise in Computational Linguistics and Information Retrieval applied to European languages and using Latin and Cyrillic alphabets, this was our first attempt on East Asian languages. Our main goal was to study the particularities and distinctive characteristics of Japanese, Chinese and Korean, specially focusing on the similarities and differences with European languages, and carry out research on CLIR tasks which include those languages. The basic idea behind our participation in NTCIR is to test if the same familiar linguisticbased techniques may also applicable to East Asian languages, and study the necessary adaptations
Scribble-based Domain Adaptation via Co-segmentation
Although deep convolutional networks have reached state-of-the-art
performance in many medical image segmentation tasks, they have typically
demonstrated poor generalisation capability. To be able to generalise from one
domain (e.g. one imaging modality) to another, domain adaptation has to be
performed. While supervised methods may lead to good performance, they require
to fully annotate additional data which may not be an option in practice. In
contrast, unsupervised methods don't need additional annotations but are
usually unstable and hard to train. In this work, we propose a novel
weakly-supervised method. Instead of requiring detailed but time-consuming
annotations, scribbles on the target domain are used to perform domain
adaptation. This paper introduces a new formulation of domain adaptation based
on structured learning and co-segmentation. Our method is easy to train, thanks
to the introduction of a regularised loss. The framework is validated on
Vestibular Schwannoma segmentation (T1 to T2 scans). Our proposed method
outperforms unsupervised approaches and achieves comparable performance to a
fully-supervised approach.Comment: Accepted at MICCAI 202
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