267 research outputs found
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
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Deep learning for cardiac image segmentation: A review
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective
For medical image segmentation, contrastive learning is the dominant practice
to improve the quality of visual representations by contrasting semantically
similar and dissimilar pairs of samples. This is enabled by the observation
that without accessing ground truth label, negative examples with truly
dissimilar anatomical features, if sampled, can significantly improve the
performance. In reality, however, these samples may come from similar
anatomical features and the models may struggle to distinguish the minority
tail-class samples, making the tail classes more prone to misclassification,
both of which typically lead to model collapse. In this paper, we propose ARCO,
a semi-supervised contrastive learning (CL) framework with stratified group
sampling theory in medical image segmentation. In particular, we first propose
building ARCO through the concept of variance-reduced estimation, and show that
certain variance-reduction techniques are particularly beneficial in medical
image segmentation tasks with extremely limited labels. Furthermore, we
theoretically prove these sampling techniques are universal in variance
reduction. Finally, we experimentally validate our approaches on three
benchmark datasets with different label settings, and our methods consistently
outperform state-of-the-art semi-supervised methods. Additionally, we augment
the CL frameworks with these sampling techniques and demonstrate significant
gains over previous methods. We believe our work is an important step towards
semi-supervised medical image segmentation by quantifying the limitation of
current self-supervision objectives for accomplishing medical image analysis
tasks
Task adapted reconstruction for inverse problems
The paper considers the problem of performing a task defined on a model
parameter that is only observed indirectly through noisy data in an ill-posed
inverse problem. A key aspect is to formalize the steps of reconstruction and
task as appropriate estimators (non-randomized decision rules) in statistical
estimation problems. The implementation makes use of (deep) neural networks to
provide a differentiable parametrization of the family of estimators for both
steps. These networks are combined and jointly trained against suitable
supervised training data in order to minimize a joint differentiable loss
function, resulting in an end-to-end task adapted reconstruction method. The
suggested framework is generic, yet adaptable, with a plug-and-play structure
for adjusting both the inverse problem and the task at hand. More precisely,
the data model (forward operator and statistical model of the noise) associated
with the inverse problem is exchangeable, e.g., by using neural network
architecture given by a learned iterative method. Furthermore, any task that is
encodable as a trainable neural network can be used. The approach is
demonstrated on joint tomographic image reconstruction, classification and
joint tomographic image reconstruction segmentation
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