2,237 research outputs found
Machine learning methods for histopathological image analysis
Abundant accumulation of digital histopathological images has led to the
increased demand for their analysis, such as computer-aided diagnosis using
machine learning techniques. However, digital pathological images and related
tasks have some issues to be considered. In this mini-review, we introduce the
application of digital pathological image analysis using machine learning
algorithms, address some problems specific to such analysis, and propose
possible solutions.Comment: 23 pages, 4 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
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
Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification
Network biology has been successfully used to help reveal complex mechanisms
of disease, especially cancer. On the other hand, network biology requires
in-depth knowledge to construct disease-specific networks, but our current
knowledge is very limited even with the recent advances in human cancer
biology. Deep learning has shown a great potential to address the difficult
situation like this. However, deep learning technologies conventionally use
grid-like structured data, thus application of deep learning technologies to
the classification of human disease subtypes is yet to be explored. Recently,
graph based deep learning techniques have emerged, which becomes an opportunity
to leverage analyses in network biology. In this paper, we proposed a hybrid
model, which integrates two key components 1) graph convolution neural network
(graph CNN) and 2) relation network (RN). We utilize graph CNN as a component
to learn expression patterns of cooperative gene community, and RN as a
component to learn associations between learned patterns. The proposed model is
applied to the PAM50 breast cancer subtype classification task, the standard
breast cancer subtype classification of clinical utility. In experiments of
both subtype classification and patient survival analysis, our proposed method
achieved significantly better performances than existing methods. We believe
that this work is an important starting point to realize the upcoming
personalized medicine.Comment: 8 pages, To be published in proceeding of IJCAI 201
An Aggregation of Aggregation Methods in Computational Pathology
Image analysis and machine learning algorithms operating on multi-gigapixel
whole-slide images (WSIs) often process a large number of tiles (sub-images)
and require aggregating predictions from the tiles in order to predict
WSI-level labels. In this paper, we present a review of existing literature on
various types of aggregation methods with a view to help guide future research
in the area of computational pathology (CPath). We propose a general CPath
workflow with three pathways that consider multiple levels and types of data
and the nature of computation to analyse WSIs for predictive modelling. We
categorize aggregation methods according to the context and representation of
the data, features of computational modules and CPath use cases. We compare and
contrast different methods based on the principle of multiple instance
learning, perhaps the most commonly used aggregation method, covering a wide
range of CPath literature. To provide a fair comparison, we consider a specific
WSI-level prediction task and compare various aggregation methods for that
task. Finally, we conclude with a list of objectives and desirable attributes
of aggregation methods in general, pros and cons of the various approaches,
some recommendations and possible future directions.Comment: 32 pages, 4 figure
Informative sample generation using class aware generative adversarial networks for classification of chest Xrays
Training robust deep learning (DL) systems for disease detection from medical
images is challenging due to limited images covering different disease types
and severity. The problem is especially acute, where there is a severe class
imbalance. We propose an active learning (AL) framework to select most
informative samples for training our model using a Bayesian neural network.
Informative samples are then used within a novel class aware generative
adversarial network (CAGAN) to generate realistic chest xray images for data
augmentation by transferring characteristics from one class label to another.
Experiments show our proposed AL framework is able to achieve state-of-the-art
performance by using about of the full dataset, thus saving significant
time and effort over conventional methods
Impact of Image Preprocessing Methods and Deep Learning Models for Classifying Histopathological Breast Cancer Images
Early diagnosis of cancer is very important as it significantly increases the chances of
appropriate treatment and survival. To this end, Deep Learning models are increasingly used in the
classification and segmentation of histopathological images, as they obtain high accuracy index and
can help specialists. In most cases, images need to be preprocessed for these models to work correctly.
In this paper, a comparative study of different preprocessing methods and deep learning models for
a set of breast cancer images is presented. For this purpose, the statistical test ANOVA with data
obtained from the performance of five different deep learning models is analyzed. An important
conclusion from this test can be obtained; from the point of view of the accuracy of the system, the
main repercussion is the deep learning models used, however, the filter used for the preprocessing of
the image, has no statistical significance for the behavior of the system.Spanish Government PID2021-128317OB-I00Government of Andalusia P20-0016
CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images
The visual examination of tissue biopsy sections is fundamental for cancer
diagnosis, with pathologists analyzing sections at multiple magnifications to
discern tumor cells and their subtypes. However, existing attention-based
multiple instance learning (MIL) models, used for analyzing Whole Slide Images
(WSIs) in cancer diagnostics, often overlook the contextual information of
tumor and neighboring tiles, leading to misclassifications. To address this, we
propose the Context-Aware Multiple Instance Learning (CAMIL) architecture.
CAMIL incorporates neighbor-constrained attention to consider dependencies
among tiles within a WSI and integrates contextual constraints as prior
knowledge into the MIL model. We evaluated CAMIL on subtyping non-small cell
lung cancer (TCGA-NSCLC) and detecting lymph node (CAMELYON16) metastasis,
achieving test AUCs of 0.959\% and 0.975\%, respectively, outperforming other
state-of-the-art methods. Additionally, CAMIL enhances model interpretability
by identifying regions of high diagnostic value.Comment: 16 pages, 4 figure
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