680 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
Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification
Convolutional Neural Networks (CNN) are state-of-the-art models for many
image classification tasks. However, to recognize cancer subtypes
automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images
(WSI) is currently computationally impossible. The differentiation of cancer
subtypes is based on cellular-level visual features observed on image patch
scale. Therefore, we argue that in this situation, training a patch-level
classifier on image patches will perform better than or similar to an
image-level classifier. The challenge becomes how to intelligently combine
patch-level classification results and model the fact that not all patches will
be discriminative. We propose to train a decision fusion model to aggregate
patch-level predictions given by patch-level CNNs, which to the best of our
knowledge has not been shown before. Furthermore, we formulate a novel
Expectation-Maximization (EM) based method that automatically locates
discriminative patches robustly by utilizing the spatial relationships of
patches. We apply our method to the classification of glioma and non-small-cell
lung carcinoma cases into subtypes. The classification accuracy of our method
is similar to the inter-observer agreement between pathologists. Although it is
impossible to train CNNs on WSIs, we experimentally demonstrate using a
comparable non-cancer dataset of smaller images that a patch-based CNN can
outperform an image-based CNN
HistoSegCap: Capsules for Weakly-Supervised Semantic Segmentation of Histological Tissue Type in Whole Slide Images
Digital pathology involves converting physical tissue slides into
high-resolution Whole Slide Images (WSIs), which pathologists analyze for
disease-affected tissues. However, large histology slides with numerous
microscopic fields pose challenges for visual search. To aid pathologists,
Computer Aided Diagnosis (CAD) systems offer visual assistance in efficiently
examining WSIs and identifying diagnostically relevant regions. This paper
presents a novel histopathological image analysis method employing Weakly
Supervised Semantic Segmentation (WSSS) based on Capsule Networks, the first
such application. The proposed model is evaluated using the Atlas of Digital
Pathology (ADP) dataset and its performance is compared with other
histopathological semantic segmentation methodologies. The findings underscore
the potential of Capsule Networks in enhancing the precision and efficiency of
histopathological image analysis. Experimental results show that the proposed
model outperforms traditional methods in terms of accuracy and the mean
Intersection-over-Union (mIoU) metric
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Breast cancer has reached the highest incidence rate worldwide among all
malignancies since 2020. Breast imaging plays a significant role in early
diagnosis and intervention to improve the outcome of breast cancer patients. In
the past decade, deep learning has shown remarkable progress in breast cancer
imaging analysis, holding great promise in interpreting the rich information
and complex context of breast imaging modalities. Considering the rapid
improvement in the deep learning technology and the increasing severity of
breast cancer, it is critical to summarize past progress and identify future
challenges to be addressed. In this paper, we provide an extensive survey of
deep learning-based breast cancer imaging research, covering studies on
mammogram, ultrasound, magnetic resonance imaging, and digital pathology images
over the past decade. The major deep learning methods, publicly available
datasets, and applications on imaging-based screening, diagnosis, treatment
response prediction, and prognosis are described in detail. Drawn from the
findings of this survey, we present a comprehensive discussion of the
challenges and potential avenues for future research in deep learning-based
breast cancer imaging.Comment: Survey, 41 page
Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images
Multiple instance learning (MIL) models have achieved remarkable success in
analyzing whole slide images (WSIs) for disease classification problems.
However, with regard to gigapixel WSI classification problems, current MIL
models are often incapable of differentiating a WSI with extremely small tumor
lesions. This minute tumor-to-normal area ratio in a MIL bag inhibits the
attention mechanism from properly weighting the areas corresponding to minor
tumor lesions. To overcome this challenge, we propose salient instance
inference MIL (SiiMIL), a weakly-supervised MIL model for WSI classification.
Our method initially learns representations of normal WSIs, and it then
compares the normal WSIs representations with all the input patches to infer
the salient instances of the input WSI. Finally, it employs attention-based MIL
to perform the slide-level classification based on the selected patches of the
WSI. Our experiments imply that SiiMIL can accurately identify tumor instances,
which could only take up less than 1% of a WSI, so that the ratio of tumor to
normal instances within a bag can increase by two to four times. It is worth
mentioning that it performs equally well for large tumor lesions. As a result,
SiiMIL achieves a significant improvement in performance over the
state-of-the-art MIL methods
Computational Pathology: A Survey Review and The Way Forward
Computational Pathology CPath is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical
histopathology images. The main objective for CPath is to develop
infrastructure and workflows of digital diagnostics as an assistive CAD system
for clinical pathology, facilitating transformational changes in the diagnosis
and treatment of cancer that are mainly address by CPath tools. With
evergrowing developments in deep learning and computer vision algorithms, and
the ease of the data flow from digital pathology, currently CPath is witnessing
a paradigm shift. Despite the sheer volume of engineering and scientific works
being introduced for cancer image analysis, there is still a considerable gap
of adopting and integrating these algorithms in clinical practice. This raises
a significant question regarding the direction and trends that are undertaken
in CPath. In this article we provide a comprehensive review of more than 800
papers to address the challenges faced in problem design all-the-way to the
application and implementation viewpoints. We have catalogued each paper into a
model-card by examining the key works and challenges faced to layout the
current landscape in CPath. We hope this helps the community to locate relevant
works and facilitate understanding of the field's future directions. In a
nutshell, we oversee the CPath developments in cycle of stages which are
required to be cohesively linked together to address the challenges associated
with such multidisciplinary science. We overview this cycle from different
perspectives of data-centric, model-centric, and application-centric problems.
We finally sketch remaining challenges and provide directions for future
technical developments and clinical integration of CPath
(https://github.com/AtlasAnalyticsLab/CPath_Survey).Comment: Accepted in Elsevier Journal of Pathology Informatics (JPI) 202
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