583 research outputs found
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
Noise transfer for unsupervised domain adaptation of retinal OCT images
Optical coherence tomography (OCT) imaging from different camera devices
causes challenging domain shifts and can cause a severe drop in accuracy for
machine learning models. In this work, we introduce a minimal noise adaptation
method based on a singular value decomposition (SVDNA) to overcome the domain
gap between target domains from three different device manufacturers in retinal
OCT imaging. Our method utilizes the difference in noise structure to
successfully bridge the domain gap between different OCT devices and transfer
the style from unlabeled target domain images to source images for which manual
annotations are available. We demonstrate how this method, despite its
simplicity, compares or even outperforms state-of-the-art unsupervised domain
adaptation methods for semantic segmentation on a public OCT dataset. SVDNA can
be integrated with just a few lines of code into the augmentation pipeline of
any network which is in contrast to many state-of-the-art domain adaptation
methods which often need to change the underlying model architecture or train a
separate style transfer model. The full code implementation for SVDNA is
available at https://github.com/ValentinKoch/SVDNA.Comment: published at MICCAI 202
A Self-Training Framework for Glaucoma Grading In OCT B-Scans
[EN] In this paper, we present a self-training-based framework for glaucoma grading using OCT B-scans under the presence of domain shift. Particularly, the proposed two-step learning methodology resorts to pseudo-labels generated during the first step to augment the training dataset on the target domain, which is then used to train the final target model. This allows transferring knowledge-domain from the unlabeled data. Additionally, we propose a novel glaucoma-specific backbone which introduces residual and attention modules via skip-connections to refine the embedding features of the latent space. By doing this, our model is capable of improving state-of-the-art from a quantitative and interpretability perspective. The reported results demonstrate that the proposed learning strategy can boost the performance of the model on the target dataset without incurring in additional annotation steps, by using only labels from the source examples. Our model consistently outperforms the baseline by 1¿3% across different metrics and bridges the gap with respect to training the model on the labeled target data.We gratefully acknowledge the support of the Generalitat
Valenciana (GVA) for the donation of the DGX A100 used for
this work, action co-financed by the European Union through
the Programa Operativo del Fondo Europeo de Desarrollo
Regional (FEDER) de la Comunitat Valenciana 2014-2020
(IDIFEDER/2020/030).García-Pardo, JG.; Colomer, A.; Verdú-Monedero, R.; Dolz, J.; Naranjo Ornedo, V. (2021). A Self-Training Framework for Glaucoma Grading In OCT B-Scans. IEEE. 1281-1285. https://doi.org/10.23919/EUSIPCO54536.2021.9616159S1281128
Co-Teaching for Unsupervised Domain Adaptation and Expansion
Unsupervised Domain Adaptation (UDA) is known to trade a model's performance
on a source domain for improving its performance on a target domain. To resolve
the issue, Unsupervised Domain Expansion (UDE) has been proposed recently to
adapt the model for the target domain as UDA does, and in the meantime maintain
its performance on the source domain. For both UDA and UDE, a model tailored to
a given domain, let it be the source or the target domain, is assumed to well
handle samples from the given domain. We question the assumption by reporting
the existence of cross-domain visual ambiguity: Due to the lack of a crystally
clear boundary between the two domains, samples from one domain can be visually
close to the other domain. We exploit this finding and accordingly propose in
this paper Co-Teaching (CT) that consists of knowledge distillation based CT
(kdCT) and mixup based CT (miCT). Specifically, kdCT transfers knowledge from a
leader-teacher network and an assistant-teacher network to a student network,
so the cross-domain visual ambiguity will be better handled by the student.
Meanwhile, miCT further enhances the generalization ability of the student.
Comprehensive experiments on two image-classification benchmarks and two
driving-scene-segmentation benchmarks justify the viability of the proposed
method
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine
Artificial intelligence (AI) continues to transform data analysis in many
domains. Progress in each domain is driven by a growing body of annotated data,
increased computational resources, and technological innovations. In medicine,
the sensitivity of the data, the complexity of the tasks, the potentially high
stakes, and a requirement of accountability give rise to a particular set of
challenges. In this review, we focus on three key methodological approaches
that address some of the particular challenges in AI-driven medical decision
making. (1) Explainable AI aims to produce a human-interpretable justification
for each output. Such models increase confidence if the results appear
plausible and match the clinicians expectations. However, the absence of a
plausible explanation does not imply an inaccurate model. Especially in highly
non-linear, complex models that are tuned to maximize accuracy, such
interpretable representations only reflect a small portion of the
justification. (2) Domain adaptation and transfer learning enable AI models to
be trained and applied across multiple domains. For example, a classification
task based on images acquired on different acquisition hardware. (3) Federated
learning enables learning large-scale models without exposing sensitive
personal health information. Unlike centralized AI learning, where the
centralized learning machine has access to the entire training data, the
federated learning process iteratively updates models across multiple sites by
exchanging only parameter updates, not personal health data. This narrative
review covers the basic concepts, highlights relevant corner-stone and
state-of-the-art research in the field, and discusses perspectives.Comment: This paper is accepted in IEEE CAA Journal of Automatica Sinica, Nov.
10 202
Image Quality Assessment for Population Cardiac MRI: From Detection to Synthesis
Cardiac magnetic resonance (CMR) images play a growing role in diagnostic imaging of cardiovascular diseases. Left Ventricular (LV) cardiac anatomy and function are widely used for diagnosis and monitoring disease progression in cardiology and to assess the patient's response to cardiac surgery and interventional procedures. For population imaging studies, CMR is arguably the most comprehensive imaging modality for non-invasive and non-ionising imaging of the heart and great vessels and, hence, most suited for population imaging cohorts. Due to insufficient radiographer's experience in planning a scan, natural cardiac muscle contraction, breathing motion, and imperfect triggering, CMR can display incomplete LV coverage, which hampers quantitative LV characterization and diagnostic accuracy.
To tackle this limitation and enhance the accuracy and robustness of the automated cardiac volume and functional assessment, this thesis focuses on the development and application of state-of-the-art deep learning (DL) techniques in cardiac imaging. Specifically, we propose new image feature representation types that are learnt with DL models and aimed at highlighting the CMR image quality cross-dataset. These representations are also intended to estimate the CMR image quality for better interpretation and analysis. Moreover, we investigate how quantitative analysis can benefit when these learnt image representations are used in image synthesis.
Specifically, a 3D fisher discriminative representation is introduced to identify CMR image quality in the UK Biobank cardiac data. Additionally, a novel adversarial learning (AL) framework is introduced for the cross-dataset CMR image quality assessment and we show that the common representations learnt by AL can be useful and informative for cross-dataset CMR image analysis. Moreover, we utilize the dataset invariance (DI) representations for CMR volumes interpolation by introducing a novel generative adversarial nets (GANs) based image synthesis framework, which enhance the CMR image quality cross-dataset
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