608 research outputs found
A Deep Learning Approach for Breast Invasive Ductal Carcinoma Detection and Lymphoma Multi-Classification in Histological Images
Accurately identifying and categorizing cancer structures/sub-types in histological images is an important clinical task involving a considerable workload and a specific subspecialty of pathologists. Digitizing pathology is a current trend that provides large amounts of visual data allowing a faster and more precise diagnosis through the development of automatic image analysis techniques. Recent studies have shown promising results for the automatic analysis of cancer tissue by using deep learning strategies that automatically extract and organize the discriminative information from the data. This paper explores deep learning methods for the automatic analysis of Hematoxylin and Eosin stained histological images of breast cancer and lymphoma. In particular, a deep learning approach is proposed for two different use cases: the detection of invasive ductal carcinoma in breast histological images and the classification of lymphoma sub-types. Both use cases have been addressed by adopting a residual convolutional neural network that is part of a convolutional autoencoder network (i.e., FusionNet). The performances have been evaluated on the public datasets of digital histological images and have been compared with those obtained by using different deep neural networks (UNet and ResNet). Additionally, comparisons with the state of the art have been considered, in accordance with different deep learning approaches. The experimental results show an improvement of 5.06% in F-measure score for the detection task and an improvement of 1.09% in the accuracy measure for the classification task
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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Evaluation of Colour Pre-processing on Patch-Based Classification of H&E-Stained Images
This paper compares the effects of colour pre-processing on the classification performance of H&E-stained images. Variations in the tissue preparation procedures, acquisition systems, stain conditions and reagents are all source of artifacts that can affect negatively computer-based classification. Pre-processing methods such as colour constancy, transfer and deconvolution have been proposed to compensate the artifacts. In this paper we compare quantitatively the combined effect of six colour pre-processing procedures and 12 colour texture descriptors on patch-based classification of H&E-stained images. We found that colour pre-processing had negative effects on accuracy in most cases – particularly when used with colour descriptors. However, some pre-processing procedures proved beneficial when employed in conjunction with classic texture descriptors such as co-occurrence matrices, Gabor filters and Local Binary Patterns
PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review
Breast cancer (BC) is a heterogeneous malignancy that still represents the second cause of cancer-related death among women worldwide. Due to the heterogeneity of BC, the correct identification of valuable biomarkers able to predict tumor biology and the best treatment approaches are still far from clear. Although molecular imaging with positron emission tomography/computed tomography (PET/CT) has improved the characterization of BC, these methods are not free from drawbacks. In recent years, radiomics and artificial intelligence (AI) have been playing an important role in the detection of several features normally unseen by the human eye in medical images. The present review provides a summary of the current status of radiomics and AI in different clinical settings of BC. A systematic search of PubMed, Web of Science and Scopus was conducted, including all articles published in English that explored radiomics and AI analyses of PET/CT images in BC. Several studies have demonstrated the potential role of such new features for the staging and prognosis as well as the assessment of biological characteristics. Radiomics and AI features appear to be promising in different clinical settings of BC, although larger prospective trials are needed to confirm and to standardize this evidence
Deep Learning for Classification of Brain Tumor Histopathological Images
Histopathological image classification has been at the forefront of medical research. We evaluated several deep and non-deep learning models for brain tumor histopathological image classification. The challenges were characterized by an insufficient amount of training data and identical glioma features. We employed transfer learning to tackle these challenges. We also employed some state-of-the-art non-deep learning classifiers on histogram of gradient features extracted from our images, as well as features extracted using CNN activations. Data augmentation was utilized in our study. We obtained an 82% accuracy with DenseNet-201 as our best for the deep learning models and an 83.8% accuracy with ANN for the non-deep learning classifiers. The average of the diagonals of the confusion matrices for each model was calculated as their accuracy. The performance metrics criteria in this study are our model’s precision in classifying each class and their average classification accuracy. Our result emphasizes the significance of deep learning as an invaluable tool for histopathological image studies
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
Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images
Histopathological characterization of colorectal polyps is an important
principle for determining the risk of colorectal cancer and future rates of
surveillance for patients. This characterization is time-intensive, requires
years of specialized training, and suffers from significant inter-observer and
intra-observer variability. In this work, we built an automatic
image-understanding method that can accurately classify different types of
colorectal polyps in whole-slide histology images to help pathologists with
histopathological characterization and diagnosis of colorectal polyps. The
proposed image-understanding method is based on deep-learning techniques, which
rely on numerous levels of abstraction for data representation and have shown
state-of-the-art results for various image analysis tasks. Our
image-understanding method covers all five polyp types (hyperplastic polyp,
sessile serrated polyp, traditional serrated adenoma, tubular adenoma, and
tubulovillous/villous adenoma) that are included in the US multi-society task
force guidelines for colorectal cancer risk assessment and surveillance, and
encompasses the most common occurrences of colorectal polyps. Our evaluation on
239 independent test samples shows our proposed method can identify the types
of colorectal polyps in whole-slide images with a high efficacy (accuracy:
93.0%, precision: 89.7%, recall: 88.3%, F1 score: 88.8%). The presented method
in this paper can reduce the cognitive burden on pathologists and improve their
accuracy and efficiency in histopathological characterization of colorectal
polyps, and in subsequent risk assessment and follow-up recommendations
Improving biomarker assessment in breast pathology
The accuracy of prognostic and therapy-predictive biomarker assessment in breast tumours is
crucial for management and therapy decision in patients with breast cancer. In this thesis,
biomarkers used in clinical practice with emphasise on Ki67 and HER2 were studied using
several methods including immunocytochemistry, in situ
hybridisation, gene expression assays and digital image analysis, with the overall aim to
improve routine biomarker evaluation and clarify the prognostic potential in early breast
cancer.
In paper I, we reported discordances in biomarker status from aspiration cytology and paired
surgical specimens from breast tumours. The limited prognostic potential of
immunocytochemistry-based Ki67 scoring demonstrated that immunohistochemistry on
resected specimens is the superior method for Ki67 evaluation. In addition, neither of the
methods were sufficient to predict molecular subtype. Following this in paper II, biomarker
agreement between core needle biopsies and subsequent specimens was investigated, both in
the adjuvant and neoadjuvant setting. Discordances in Ki67 and HER2 status between core
biopsies and paired specimens suggested that these biomarkers should be re-tested on all
surgical breast cancer specimens. In paper III, digital image analysis using a virtual double
staining software was used to compare methods for assessment of proliferative activity,
including mitotic counts, Ki67 and the alternative marker PHH3, in different tumour regions
(hot spot, invasive edge and whole section). Digital image analysis using virtual double staining
of hot spot Ki67 outperformed the alternative markers of proliferation, especially in
discriminating luminal B from luminal A tumours. Replacing mitosis in histological grade with
hot spot-scored Ki67 added significant prognostic information. Following these findings, the
optimal definition of a hot spot for Ki67 scoring using virtual double staining in relation to
molecular subtype and outcome was investigated in paper IV. With the growing evidence of
global scoring as a superior method to improve reproducibility of Ki67 scoring, a different
digital image analysis software (QuPath) was also used for comparison. Altogether, we found
that automated global scoring of Ki67 using QuPath had independent prognostic potential
compared to even the best virtual double staining hot spot algorithm, and is also a practical
method for routine Ki67 scoring in breast pathology. In paper V, the clinical value of HER2
status was investigated in a unique trastuzumab-treated HER2-positive cohort, on the protein,
mRNA and DNA levels. The results demonstrated that low levels of ERBB2 mRNA but neither
HER2 copy numbers, HER2 ratio nor ER status, was associated with risk of recurrence among
anti-HER2 treated breast cancer patients.
In conclusion, we have identified important clinical aspects of Ki67 and HER2 evaluation and
provided methods to improve the prognostic potential of Ki67 using digital image analysis. In
addition to protein expression of routine biomarkers, mRNA levels by targeted gene expression
assays may add further prognostic value in early breast cance
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