528 research outputs found
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
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
Learning Deep Neural Networks for Enhanced Prostate Histological Image Analysis
In recent years, deep convolutional neural networks (CNNs) have shown
promise for improving prostate cancer diagnosis by enabling quantitative
histopathology through digital pathology. However, there are a number of
factors that limit the widespread adoption and clinical utility of deep learning
for digital pathology. One of these limitations is the requirement for large
labelled training datasets which are expensive to construct due to limited availability
of the requisite expertise. Additionally, digital pathology applications
typically require the digitisation of histological slides at high magnifications.
This process can be challenging especially when digitising large histological
slides such as prostatectomies.
This work studies and addresses these issues in two important applications
of digital pathology: prostate nuclei detection and cell type classification. We
study the performance of CNNs at different magnifications and demonstrate
that it is possible to perform nuclei detection in low magnification prostate
histopathology using CNNs with minimal loss in accuracy. We then study the
training of prostate nuclei detectors in the small data setting and demonstrate
that although it is possible to train nuclei detectors with minimal data, the
models will be sensitive to hyperparameter choice and therefore may not generalise
well. Instead, we show that pre-training the CNNs with colon histology
data makes them more robust to hyperparameter choice.
We then study the CNN performance for prostate cell type classification
using supervised, transfer and semi-supervised learning in the small data setting.
Our results show that transfer learning can be detrimental to performance but semi-supervised learning is able to provide significant improvements to the
learning curve, allowing the training of neural networks with modest amounts
of labelled data. We then propose a novel semi-supervised learning method
called Deeply-supervised Exemplar CNNs and demonstrate their ability to improve
the cell type classifier learning curves at a much better rate than previous
semi-supervised neural network methods
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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