418 research outputs found
Advancing efficiency and robustness of neural networks for imaging
Enabling machines to see and analyze the world is a longstanding research objective. Advances in computer vision have the potential of influencing many aspects of our lives as they can enable machines to tackle a variety of tasks. Great progress in computer vision has been made, catalyzed by recent progress in machine learning and especially the breakthroughs achieved by deep artificial neural networks.
Goal of this work is to alleviate limitations of deep neural networks that hinder their large-scale adoption for real-world applications. To this end, it investigates methodologies for constructing and training deep neural networks with low computational requirements. Moreover, it explores strategies for achieving robust performance on unseen data. Of particular interest is the application of segmenting volumetric medical scans because of the technical challenges it imposes, as well as its clinical importance. The developed methodologies are generic and of relevance to a broader computer vision and machine learning audience.
More specifically, this work introduces an efficient 3D convolutional neural network architecture, which achieves high performance for segmentation of volumetric medical images, an application previously hindered by high computational requirements of 3D networks. It then investigates sensitivity of network performance on hyper-parameter configuration, which we interpret as overfitting the model configuration to the data available during development. It is shown that ensembling a set of models with diverse configurations mitigates this and improves generalization. The thesis then explores how to utilize unlabelled data for learning representations that generalize better. It investigates domain adaptation and introduces an architecture for adversarial networks tailored for adaptation of segmentation networks. Finally, a novel semi-supervised learning method is proposed that introduces a graph in the latent space of a neural network to capture relations between labelled and unlabelled samples. It then regularizes the embedding to form a compact cluster per class, which improves generalization.Open Acces
Volumetric medical image segmentation with deep learning pipelines
Automated semantic segmentation in the domain of medical imaging can enable a faster, more reliable, and more affordable clinical workflow. Fully convolutional networks (FCNs) have been heavily used in this area due to the level of success that they have achieved. In this work, we first leverage recent architectural innovations to make an initial segmentation: (i) spatial and channel-wise squeeze and excitation mechanism; (ii) a 3D U-Net++ network and deep supervision. Second, we use classical methods for refining the initial segmentation: (i) spatial normalization and (ii) local 3D refinement network applied to patches. Finally, we put our methods together in a novel segmentation pipeline. We train and evaluate our models and pipelines on a dataset of a 120 abdominal magnetic resonance - volumetric - images (MRIs). The goal is to segment five different organs of interest (ORI): liver, kidneys, stomach, duodenum, and large bowel. Our experiments show that we can generate full resolution segmentation of comparable quality to the state-of-the-art methods without adding computational cost.Includes bibliographical references
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
The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detection
Artificial intelligence represents a new frontier in human medicine that
could save more lives and reduce the costs, thereby increasing accessibility.
As a consequence, the rate of advancement of AI in cancer medical imaging and
more particularly tissue pathology has exploded, opening it to ethical and
technical questions that could impede its adoption into existing systems. In
order to chart the path of AI in its application to cancer tissue imaging, we
review current work and identify how it can improve cancer pathology
diagnostics and research. In this review, we identify 5 core tasks that models
are developed for, including regression, classification, segmentation,
generation, and compression tasks. We address the benefits and challenges that
such methods face, and how they can be adapted for use in cancer prevention and
treatment. The studies looked at in this paper represent the beginning of this
field and future experiments will build on the foundations that we highlight
Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty
The clinical interest is often to measure the volume of a structure, which is
typically derived from a segmentation. In order to evaluate and compare
segmentation methods, the similarity between a segmentation and a predefined
ground truth is measured using popular discrete metrics, such as the Dice
score. Recent segmentation methods use a differentiable surrogate metric, such
as soft Dice, as part of the loss function during the learning phase. In this
work, we first briefly describe how to derive volume estimates from a
segmentation that is, potentially, inherently uncertain or ambiguous. This is
followed by a theoretical analysis and an experimental validation linking the
inherent uncertainty to common loss functions for training CNNs, namely
cross-entropy and soft Dice. We find that, even though soft Dice optimization
leads to an improved performance with respect to the Dice score and other
measures, it may introduce a volume bias for tasks with high inherent
uncertainty. These findings indicate some of the method's clinical limitations
and suggest doing a closer ad-hoc volume analysis with an optional
re-calibration step.Comment: 18 pages, 7 figures, 3 tables, published in Elsevier Medical Image
Analysis (2021
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