1,353 research outputs found
LDCSF: Local depth convolution-based Swim framework for classifying multi-label histopathology images
Histopathological images are the gold standard for diagnosing liver cancer.
However, the accuracy of fully digital diagnosis in computational pathology
needs to be improved. In this paper, in order to solve the problem of
multi-label and low classification accuracy of histopathology images, we
propose a locally deep convolutional Swim framework (LDCSF) to classify
multi-label histopathology images. In order to be able to provide local field
of view diagnostic results, we propose the LDCSF model, which consists of a
Swin transformer module, a local depth convolution (LDC) module, a feature
reconstruction (FR) module, and a ResNet module. The Swin transformer module
reduces the amount of computation generated by the attention mechanism by
limiting the attention to each window. The LDC then reconstructs the attention
map and performs convolution operations in multiple channels, passing the
resulting feature map to the next layer. The FR module uses the corresponding
weight coefficient vectors obtained from the channels to dot product with the
original feature map vector matrix to generate representative feature maps.
Finally, the residual network undertakes the final classification task. As a
result, the classification accuracy of LDCSF for interstitial area, necrosis,
non-tumor and tumor reached 0.9460, 0.9960, 0.9808, 0.9847, respectively.
Finally, we use the results of multi-label pathological image classification to
calculate the tumor-to-stromal ratio, which lays the foundation for the
analysis of the microenvironment of liver cancer histopathological images.
Second, we released a multilabel histopathology image of liver cancer, our code
and data are available at https://github.com/panliangrui/LSF.Comment: Submitted to BIBM202
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
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
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