23 research outputs found
Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review
The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions
Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review
Instance segmentation of nuclei and glands in the histology images is an
important step in computational pathology workflow for cancer diagnosis,
treatment planning and survival analysis. With the advent of modern hardware,
the recent availability of large-scale quality public datasets and the
community organized grand challenges have seen a surge in automated methods
focusing on domain specific challenges, which is pivotal for technology
advancements and clinical translation. In this survey, 126 papers illustrating
the AI based methods for nuclei and glands instance segmentation published in
the last five years (2017-2022) are deeply analyzed, the limitations of current
approaches and the open challenges are discussed. Moreover, the potential
future research direction is presented and the contribution of state-of-the-art
methods is summarized. Further, a generalized summary of publicly available
datasets and a detailed insights on the grand challenges illustrating the top
performing methods specific to each challenge is also provided. Besides, we
intended to give the reader current state of existing research and pointers to
the future directions in developing methods that can be used in clinical
practice enabling improved diagnosis, grading, prognosis, and treatment
planning of cancer. To the best of our knowledge, no previous work has reviewed
the instance segmentation in histology images focusing towards this direction.Comment: 60 pages, 14 figure
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
Computer aided diagnosis algorithms for digital microscopy
Automatic analysis and information extraction from an image is still a highly chal-
lenging research problem in the computer vision area, attempting to describe the
image content with computational and mathematical techniques. Moreover the in-
formation extracted from the image should be meaningful and as most discrimi-
natory as possible, since it will be used to categorize its content according to the
analysed problem. In the Medical Imaging domain this issue is even more felt
because many important decisions that affect the patient care, depend on the use-
fulness of the information extracted from the image. Manage medical image is even
more complicated not only due to the importance of the problem, but also because
it needs a fair amount of prior medical knowledge to be able to represent with data
the visual information to which pathologist refer.
Today medical decisions that impact patient care rely on the results of laboratory
tests to a greater extent than ever before, due to the marked expansion in the number
and complexity of offered tests. These developments promise to improve the care of
patients, but the more increase the number and complexity of the tests, the more
increases the possibility to misapply and misinterpret the test themselves, leading
to inappropriate diagnosis and therapies. Moreover, with the increased number of
tests also the amount of data to be analysed increases, forcing pathologists to devote
much time to the analysis of the tests themselves rather than to patient care and
the prescription of the right therapy, especially considering that most of the tests
performed are just check up tests and most of the analysed samples come from
healthy patients.
Then, a quantitative evaluation of medical images is really essential to overcome
uncertainty and subjectivity, but also to greatly reduce the amount of data and
the timing for the analysis. In the last few years, many computer assisted diagno-
sis systems have been developed, attempting to mimic pathologists by extracting
features from the images. Image analysis involves complex algorithms to identify
and characterize cells or tissues using image pattern recognition technology. This
thesis addresses the main problems associated to the digital microscopy analysis
in histology and haematology diagnosis, with the development of algorithms for the
extraction of useful information from different digital images, but able to distinguish
different biological structures in the images themselves. The proposed methods not
only aim to improve the degree of accuracy of the analysis, and reducing time, if used as the only means of diagnoses, but also they can be used as intermediate tools
for skimming the number of samples to be analysed directly from the pathologist,
or as double check systems to verify the correct results of the automated facilities
used today
Computer aided diagnosis algorithms for digital microscopy
Automatic analysis and information extraction from an image is still a highly chal-
lenging research problem in the computer vision area, attempting to describe the
image content with computational and mathematical techniques. Moreover the in-
formation extracted from the image should be meaningful and as most discrimi-
natory as possible, since it will be used to categorize its content according to the
analysed problem. In the Medical Imaging domain this issue is even more felt
because many important decisions that affect the patient care, depend on the use-
fulness of the information extracted from the image. Manage medical image is even
more complicated not only due to the importance of the problem, but also because
it needs a fair amount of prior medical knowledge to be able to represent with data
the visual information to which pathologist refer.
Today medical decisions that impact patient care rely on the results of laboratory
tests to a greater extent than ever before, due to the marked expansion in the number
and complexity of offered tests. These developments promise to improve the care of
patients, but the more increase the number and complexity of the tests, the more
increases the possibility to misapply and misinterpret the test themselves, leading
to inappropriate diagnosis and therapies. Moreover, with the increased number of
tests also the amount of data to be analysed increases, forcing pathologists to devote
much time to the analysis of the tests themselves rather than to patient care and
the prescription of the right therapy, especially considering that most of the tests
performed are just check up tests and most of the analysed samples come from
healthy patients.
Then, a quantitative evaluation of medical images is really essential to overcome
uncertainty and subjectivity, but also to greatly reduce the amount of data and
the timing for the analysis. In the last few years, many computer assisted diagno-
sis systems have been developed, attempting to mimic pathologists by extracting
features from the images. Image analysis involves complex algorithms to identify
and characterize cells or tissues using image pattern recognition technology. This
thesis addresses the main problems associated to the digital microscopy analysis
in histology and haematology diagnosis, with the development of algorithms for the
extraction of useful information from different digital images, but able to distinguish
different biological structures in the images themselves. The proposed methods not
only aim to improve the degree of accuracy of the analysis, and reducing time, if used as the only means of diagnoses, but also they can be used as intermediate tools
for skimming the number of samples to be analysed directly from the pathologist,
or as double check systems to verify the correct results of the automated facilities
used today