375 research outputs found
Generalising Deep Learning MRI Reconstruction across Different Domains
We look into robustness of deep learning based MRI reconstruction when tested
on unseen contrasts and organs. We then propose to generalise the network by
training with large publicly-available natural image datasets with synthesised
phase information to achieve high cross-domain reconstruction performance which
is competitive with domain-specific training. To explain its generalisation
mechanism, we have also analysed patch sets for different training datasets.Comment: Accepted for ISBI2019 as a 1-page abstrac
Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation
Generalising deep models to new data from new centres (termed here domains)
remains a challenge. This is largely attributed to shifts in data statistics
(domain shifts) between source and unseen domains. Recently, gradient-based
meta-learning approaches where the training data are split into meta-train and
meta-test sets to simulate and handle the domain shifts during training have
shown improved generalisation performance. However, the current fully
supervised meta-learning approaches are not scalable for medical image
segmentation, where large effort is required to create pixel-wise annotations.
Meanwhile, in a low data regime, the simulated domain shifts may not
approximate the true domain shifts well across source and unseen domains. To
address this problem, we propose a novel semi-supervised meta-learning
framework with disentanglement. We explicitly model the representations related
to domain shifts. Disentangling the representations and combining them to
reconstruct the input image allows unlabeled data to be used to better
approximate the true domain shifts for meta-learning. Hence, the model can
achieve better generalisation performance, especially when there is a limited
amount of labeled data. Experiments show that the proposed method is robust on
different segmentation tasks and achieves state-of-the-art generalisation
performance on two public benchmarks.Comment: Accepted by MICCAI 202
Compositionally Equivariant Representation Learning
Deep learning models often need sufficient supervision (i.e. labelled data)
in order to be trained effectively. By contrast, humans can swiftly learn to
identify important anatomy in medical images like MRI and CT scans, with
minimal guidance. This recognition capability easily generalises to new images
from different medical facilities and to new tasks in different settings. This
rapid and generalisable learning ability is largely due to the compositional
structure of image patterns in the human brain, which are not well represented
in current medical models. In this paper, we study the utilisation of
compositionality in learning more interpretable and generalisable
representations for medical image segmentation. Overall, we propose that the
underlying generative factors that are used to generate the medical images
satisfy compositional equivariance property, where each factor is compositional
(e.g. corresponds to the structures in human anatomy) and also equivariant to
the task. Hence, a good representation that approximates well the ground truth
factor has to be compositionally equivariant. By modelling the compositional
representations with learnable von-Mises-Fisher (vMF) kernels, we explore how
different design and learning biases can be used to enforce the representations
to be more compositionally equivariant under un-, weakly-, and semi-supervised
settings. Extensive results show that our methods achieve the best performance
over several strong baselines on the task of semi-supervised domain-generalised
medical image segmentation. Code will be made publicly available upon
acceptance at https://github.com/vios-s.Comment: Submitted. 10 pages. arXiv admin note: text overlap with
arXiv:2206.1453
Generalizable deep learning based medical image segmentation
Deep learning is revolutionizing medical image analysis and interpretation. However, its real-world deployment is often hindered by the poor generalization to unseen domains (new imaging modalities and protocols). This lack of generalization ability is further exacerbated by the scarcity of labeled datasets for training: Data collection and annotation can be prohibitively expensive in terms of labor and costs because label quality heavily dependents on the expertise of radiologists. Additionally, unreliable predictions caused by poor model generalization pose safety risks to clinical downstream applications.
To mitigate labeling requirements, we investigate and develop a series of techniques to strengthen the generalization ability and the data efficiency of deep medical image computing models. We further improve model accountability and identify unreliable predictions made on out-of-domain data, by designing probability calibration techniques.
In the first and the second part of thesis, we discuss two types of problems for handling unexpected domains: unsupervised domain adaptation and single-source domain generalization. For domain adaptation we present a data-efficient technique that adapts a segmentation model trained on a labeled source domain (e.g., MRI) to an unlabeled target domain (e.g., CT), using a small number of unlabeled training images from the target domain.
For domain generalization, we focus on both image reconstruction and segmentation. For image reconstruction, we design a simple and effective domain generalization technique for cross-domain MRI reconstruction, by reusing image representations learned from natural image datasets. For image segmentation, we perform causal analysis of the challenging cross-domain image segmentation problem. Guided by this causal analysis we propose an effective data-augmentation-based generalization technique for single-source domains. The proposed method outperforms existing approaches on a large variety of cross-domain image segmentation scenarios.
In the third part of the thesis, we present a novel self-supervised method for learning generic image representations that can be used to analyze unexpected objects of interest. The proposed method is designed together with a novel few-shot image segmentation framework that can segment unseen objects of interest by taking only a few labeled examples as references. Superior flexibility over conventional fully-supervised models is demonstrated by our few-shot framework: it does not require any fine-tuning on novel objects of interest. We further build a publicly available comprehensive evaluation environment for few-shot medical image segmentation.
In the fourth part of the thesis, we present a novel probability calibration model. To ensure safety in clinical settings, a deep model is expected to be able to alert human radiologists if it has low confidence, especially when confronted with out-of-domain data. To this end we present a plug-and-play model to calibrate prediction probabilities on out-of-domain data. It aligns the prediction probability in line with the actual accuracy on the test data. We evaluate our method on both artifact-corrupted images and images from an unforeseen MRI scanning protocol. Our method demonstrates improved calibration accuracy compared with the state-of-the-art method.
Finally, we summarize the major contributions and limitations of our works. We also suggest future research directions that will benefit from the works in this thesis.Open Acces
Enhancing Deep Learning Models through Tensorization: A Comprehensive Survey and Framework
The burgeoning growth of public domain data and the increasing complexity of
deep learning model architectures have underscored the need for more efficient
data representation and analysis techniques. This paper is motivated by the
work of (Helal, 2023) and aims to present a comprehensive overview of
tensorization. This transformative approach bridges the gap between the
inherently multidimensional nature of data and the simplified 2-dimensional
matrices commonly used in linear algebra-based machine learning algorithms.
This paper explores the steps involved in tensorization, multidimensional data
sources, various multiway analysis methods employed, and the benefits of these
approaches. A small example of Blind Source Separation (BSS) is presented
comparing 2-dimensional algorithms and a multiway algorithm in Python. Results
indicate that multiway analysis is more expressive. Contrary to the intuition
of the dimensionality curse, utilising multidimensional datasets in their
native form and applying multiway analysis methods grounded in multilinear
algebra reveal a profound capacity to capture intricate interrelationships
among various dimensions while, surprisingly, reducing the number of model
parameters and accelerating processing. A survey of the multi-away analysis
methods and integration with various Deep Neural Networks models is presented
using case studies in different application domains.Comment: 34 pages, 8 figures, 4 table
Disaster Analysis using Satellite Image Data with Knowledge Transfer and Semi-Supervised Learning Techniques
With the increase in frequency of disasters and crisis situations like floods, earthquake and hurricanes, the requirement to handle the situation efficiently through disaster response and humanitarian relief has increased. Disasters are mostly unpredictable in nature with respect to their impact on people and property. Moreover, the dynamic and varied nature of disasters makes it difficult to predict their impact accurately for advanced preparation of responses [104]. It is also notable that the economical loss due to natural disasters has increased in recent years, and it, along with the pure humanitarian need, is one of the reasons to research innovative approaches to the mitigation and management of disaster operations efficiently [1]
Learning Disentangled Representations in the Imaging Domain
Disentangled representation learning has been proposed as an approach to
learning general representations even in the absence of, or with limited,
supervision. A good general representation can be fine-tuned for new target
tasks using modest amounts of data, or used directly in unseen domains
achieving remarkable performance in the corresponding task. This alleviation of
the data and annotation requirements offers tantalising prospects for
applications in computer vision and healthcare. In this tutorial paper, we
motivate the need for disentangled representations, present key theory, and
detail practical building blocks and criteria for learning such
representations. We discuss applications in medical imaging and computer vision
emphasising choices made in exemplar key works. We conclude by presenting
remaining challenges and opportunities.Comment: Submitted. This paper follows a tutorial style but also surveys a
considerable (more than 200 citations) number of work
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