1,216 research outputs found
Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning
Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence
imaging technology that has the potential to increase intraoperative precision,
extend resection, and tailor surgery for malignant invasive brain tumors
because of its subcellular dimension resolution. Despite its promising
diagnostic potential, interpreting the gray tone fluorescence images can be
difficult for untrained users. In this review, we provide a detailed
description of bioinformatical analysis methodology of CLE images that begins
to assist the neurosurgeon and pathologist to rapidly connect on-the-fly
intraoperative imaging, pathology, and surgical observation into a
conclusionary system within the concept of theranostics. We present an overview
and discuss deep learning models for automatic detection of the diagnostic CLE
images and discuss various training regimes and ensemble modeling effect on the
power of deep learning predictive models. Two major approaches reviewed in this
paper include the models that can automatically classify CLE images into
diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and
models that can localize histological features on the CLE images using weakly
supervised methods. We also briefly review advances in the deep learning
approaches used for CLE image analysis in other organs. Significant advances in
speed and precision of automated diagnostic frame selection would augment the
diagnostic potential of CLE, improve operative workflow and integration into
brain tumor surgery. Such technology and bioinformatics analytics lend
themselves to improved precision, personalization, and theranostics in brain
tumor treatment.Comment: See the final version published in Frontiers in Oncology here:
https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful
Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks
Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected mosquitoes. Automation of the diagnosis process will enable accurate diagnosis of the disease and hence holds the promise of delivering reliable health-care to resource-scarce areas. Machine learning technologies have been used for automated diagnosis of malaria. We present some of our recent progresses on highly accurate classification of malaria-infected cells using deep convolutional neural networks. First, we describe image processing methods used for segmentation of red blood cells from wholeslide images. We then discuss the procedures of compiling a pathologists-curated image dataset for training deep neural network, as well as data augmentation methods used to significantly increase the size of the dataset, in light of the overfitting problem associated with training deep convolutional neural networks. We will then compare the classification accuracies obtained by deep convolutional neural networks through training, validating, and testing with various combinations of the datasets. These datasets include the original dataset and the significantly augmented datasets, which are obtained using direct interpolation, as well as indirect interpolation using automatically extracted features provided by stacked autoencoders. This chapter ends with a discussion of further research
Deep learning-enabled technologies for bioimage analysis.
Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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
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
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