435 research outputs found
Now and Future of Artificial Intelligence-based Signet Ring Cell Diagnosis: A Survey
Since signet ring cells (SRCs) are associated with high peripheral metastasis
rate and dismal survival, they play an important role in determining surgical
approaches and prognosis, while they are easily missed by even experienced
pathologists. Although automatic diagnosis SRCs based on deep learning has
received increasing attention to assist pathologists in improving the
diagnostic efficiency and accuracy, the existing works have not been
systematically overviewed, which hindered the evaluation of the gap between
algorithms and clinical applications. In this paper, we provide a survey on SRC
analysis driven by deep learning from 2008 to August 2023. Specifically, the
biological characteristics of SRCs and the challenges of automatic
identification are systemically summarized. Then, the representative algorithms
are analyzed and compared via dividing them into classification, detection, and
segmentation. Finally, for comprehensive consideration to the performance of
existing methods and the requirements for clinical assistance, we discuss the
open issues and future trends of SRC analysis. The retrospect research will
help researchers in the related fields, particularly for who without medical
science background not only to clearly find the outline of SRC analysis, but
also gain the prospect of intelligent diagnosis, resulting in accelerating the
practice and application of intelligent algorithms
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
A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learning
Histopathological images provide rich information for disease diagnosis.
Large numbers of histopathological images have been digitized into high
resolution whole slide images, opening opportunities in developing
computational image analysis tools to reduce pathologists' workload and
potentially improve inter- and intra- observer agreement. Most previous work on
whole slide image analysis has focused on classification or segmentation of
small pre-selected regions-of-interest, which requires fine-grained annotation
and is non-trivial to extend for large-scale whole slide analysis. In this
paper, we proposed a multi-resolution multiple instance learning model that
leverages saliency maps to detect suspicious regions for fine-grained grade
prediction. Instead of relying on expensive region- or pixel-level annotations,
our model can be trained end-to-end with only slide-level labels. The model is
developed on a large-scale prostate biopsy dataset containing 20,229 slides
from 830 patients. The model achieved 92.7% accuracy, 81.8% Cohen's Kappa for
benign, low grade (i.e. Grade group 1) and high grade (i.e. Grade group >= 2)
prediction, an area under the receiver operating characteristic curve (AUROC)
of 98.2% and an average precision (AP) of 97.4% for differentiating malignant
and benign slides. The model obtained an AUROC of 99.4% and an AP of 99.8% for
cancer detection on an external dataset.Comment: 9 pages, 6 figure
Artificial intelligence in digital pathology: a diagnostic test accuracy systematic review and meta-analysis
Ensuring diagnostic performance of AI models before clinical use is key to
the safe and successful adoption of these technologies. Studies reporting AI
applied to digital pathology images for diagnostic purposes have rapidly
increased in number in recent years. The aim of this work is to provide an
overview of the diagnostic accuracy of AI in digital pathology images from all
areas of pathology. This systematic review and meta-analysis included
diagnostic accuracy studies using any type of artificial intelligence applied
to whole slide images (WSIs) in any disease type. The reference standard was
diagnosis through histopathological assessment and / or immunohistochemistry.
Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. We
identified 2976 studies, of which 100 were included in the review and 48 in the
full meta-analysis. Risk of bias and concerns of applicability were assessed
using the QUADAS-2 tool. Data extraction was conducted by two investigators and
meta-analysis was performed using a bivariate random effects model. 100 studies
were identified for inclusion, equating to over 152,000 whole slide images
(WSIs) and representing many disease types. Of these, 48 studies were included
in the meta-analysis. These studies reported a mean sensitivity of 96.3% (CI
94.1-97.7) and mean specificity of 93.3% (CI 90.5-95.4) for AI. There was
substantial heterogeneity in study design and all 100 studies identified for
inclusion had at least one area at high or unclear risk of bias. This review
provides a broad overview of AI performance across applications in whole slide
imaging. However, there is huge variability in study design and available
performance data, with details around the conduct of the study and make up of
the datasets frequently missing. Overall, AI offers good accuracy when applied
to WSIs but requires more rigorous evaluation of its performance.Comment: 26 pages, 5 figures, 8 tables + Supplementary material
A attention way in Explainable methods for infant brain
Deploying reliable deep learning techniques in interdisciplinary applications
needs learned models to output accurate and ({even more importantly})
explainable predictions. Existing approaches typically explicate network
outputs in a post-hoc fashion, under an implicit assumption that faithful
explanations come from accurate predictions/classifications. We have an
opposite claim that explanations boost (or even determine) classification. That
is, end-to-end learning of explanation factors to augment discriminative
representation extraction could be a more intuitive strategy to inversely
assure fine-grained explainability, e.g., in those neuroimaging and
neuroscience studies with high-dimensional data containing noisy, redundant,
and task-irrelevant information. In this paper, we propose such an explainable
geometric deep network dubbed.Comment: Some parts of the thesis are still being revise
The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning
Introduction: Technical burdens and time-intensive review processes limit the
practical utility of video capsule endoscopy (VCE). Artificial intelligence
(AI) is poised to address these limitations, but the intersection of AI and VCE
reveals challenges that must first be overcome. We identified five challenges
to address. Challenge #1: VCE data are stochastic and contains significant
artifact. Challenge #2: VCE interpretation is cost-intensive. Challenge #3: VCE
data are inherently imbalanced. Challenge #4: Existing VCE AIMLT are
computationally cumbersome. Challenge #5: Clinicians are hesitant to accept
AIMLT that cannot explain their process.
Methods: An anatomic landmark detection model was used to test the
application of convolutional neural networks (CNNs) to the task of classifying
VCE data. We also created a tool that assists in expert annotation of VCE data.
We then created more elaborate models using different approaches including a
multi-frame approach, a CNN based on graph representation, and a few-shot
approach based on meta-learning.
Results: When used on full-length VCE footage, CNNs accurately identified
anatomic landmarks (99.1%), with gradient weighted-class activation mapping
showing the parts of each frame that the CNN used to make its decision. The
graph CNN with weakly supervised learning (accuracy 89.9%, sensitivity of
91.1%), the few-shot model (accuracy 90.8%, precision 91.4%, sensitivity
90.9%), and the multi-frame model (accuracy 97.5%, precision 91.5%, sensitivity
94.8%) performed well. Discussion: Each of these five challenges is addressed,
in part, by one of our AI-based models. Our goal of producing high performance
using lightweight models that aim to improve clinician confidence was achieved
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
With an increase in deep learning-based methods, the call for explainability
of such methods grows, especially in high-stakes decision making areas such as
medical image analysis. This survey presents an overview of eXplainable
Artificial Intelligence (XAI) used in deep learning-based medical image
analysis. A framework of XAI criteria is introduced to classify deep
learning-based medical image analysis methods. Papers on XAI techniques in
medical image analysis are then surveyed and categorized according to the
framework and according to anatomical location. The paper concludes with an
outlook of future opportunities for XAI in medical image analysis.Comment: Submitted for publication. Comments welcome by email to first autho
Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning
Aortic stenosis (AS) is a degenerative valve condition that causes
substantial morbidity and mortality. This condition is under-diagnosed and
under-treated. In clinical practice, AS is diagnosed with expert review of
transthoracic echocardiography, which produces dozens of ultrasound images of
the heart. Only some of these views show the aortic valve. To automate
screening for AS, deep networks must learn to mimic a human expert's ability to
identify views of the aortic valve then aggregate across these relevant images
to produce a study-level diagnosis. We find previous approaches to AS detection
yield insufficient accuracy due to relying on inflexible averages across
images. We further find that off-the-shelf attention-based multiple instance
learning (MIL) performs poorly. We contribute a new end-to-end MIL approach
with two key methodological innovations. First, a supervised attention
technique guides the learned attention mechanism to favor relevant views.
Second, a novel self-supervised pretraining strategy applies contrastive
learning on the representation of the whole study instead of individual images
as commonly done in prior literature. Experiments on an open-access dataset and
an external validation set show that our approach yields higher accuracy while
reducing model size.Comment: multiple-instance learning; self-supervised learning; semi-supervised
learning; medical imagin
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