404 research outputs found
Cats or CAT scans: transfer learning from natural or medical image source datasets?
Transfer learning is a widely used strategy in medical image analysis.
Instead of only training a network with a limited amount of data from the
target task of interest, we can first train the network with other, potentially
larger source datasets, creating a more robust model. The source datasets do
not have to be related to the target task. For a classification task in lung CT
images, we could use both head CT images, or images of cats, as the source.
While head CT images appear more similar to lung CT images, the number and
diversity of cat images might lead to a better model overall. In this survey we
review a number of papers that have performed similar comparisons. Although the
answer to which strategy is best seems to be "it depends", we discuss a number
of research directions we need to take as a community, to gain more
understanding of this topic.Comment: Accepted to Current Opinion in Biomedical Engineerin
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
Interpretation of immunofluorescence slides by deep learning techniques: anti-nuclear antibodies case study
Nowadays, diseases are increasing in numbers and severity by the hour.
Immunity diseases, affecting 8\% of the world population in 2017 according to
the World Health Organization (WHO), is a field in medicine worth attention due
to the high rate of disease occurrence classified under this category. This
work presents an up-to-date review of state-of-the-art immune diseases
healthcare solutions. We focus on tackling the issue with modern solutions such
as Deep Learning to detect anomalies in the early stages hence providing health
practitioners with efficient tools. We rely on advanced deep learning
techniques such as Convolutional Neural Networks (CNN) to fulfill our objective
of providing an efficient tool while providing a proficient analysis of this
solution. The proposed solution was tested and evaluated by the immunology
department in the Principal Military Hospital of Instruction of Tunis, which
considered it a very helpful tool
Deep Learning based HEp-2 Image Classification: A Comprehensive Review
Classification of HEp-2 cell patterns plays a significant role in the
indirect immunofluorescence test for identifying autoimmune diseases in the
human body. Many automatic HEp-2 cell classification methods have been proposed
in recent years, amongst which deep learning based methods have shown
impressive performance. This paper provides a comprehensive review of the
existing deep learning based HEp-2 cell image classification methods. These
methods perform HEp-2 image classification at two levels, namely, cell-level
and specimen-level. Both levels are covered in this review. At each level, the
methods are organized with a deep network usage based taxonomy. The core idea,
notable achievements, and key strengths and weaknesses of each method are
critically analyzed. Furthermore, a concise review of the existing HEp-2
datasets that are commonly used in the literature is given. The paper ends with
a discussion on novel opportunities and future research directions in this
field. It is hoped that this paper would provide readers with a thorough
reference of this novel, challenging, and thriving field.Comment: Published in Medical Image Analysi
- …