1,008 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
Deep learning features encode interpretable morphologies within histological images.
Convolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotypes from hematoxylin and eosin (H&E) whole slide images (WSIs), but the interpretation of CNNs remains difficult. Most studies have considered interpretability in a post hoc fashion, e.g. by presenting example regions with strongly predicted class labels. However, such an approach does not explain the biological features that contribute to correct predictions. To address this problem, here we investigate the interpretability of H&E-derived CNN features (the feature weights in the final layer of a transfer-learning-based architecture). While many studies have incorporated CNN features into predictive models, there has been little empirical study of their properties. We show such features can be construed as abstract morphological genes ( mones ) with strong independent associations to biological phenotypes. Many mones are specific to individual cancer types, while others are found in multiple cancers especially from related tissue types. We also observe that mone-mone correlations are strong and robustly preserved across related cancers. Importantly, linear mone-based classifiers can very accurately separate 38 distinct classes (19 tumor types and their adjacent normals, AUC = [Formula: see text] for each class prediction), and linear classifiers are also highly effective for universal tumor detection (AUC = [Formula: see text]). This linearity provides evidence that individual mones or correlated mone clusters may be associated with interpretable histopathological features or other patient characteristics. In particular, the statistical similarity of mones to gene expression values allows integrative mone analysis via expression-based bioinformatics approaches. We observe strong correlations between individual mones and individual gene expression values, notably mones associated with collagen gene expression in ovarian cancer. Mone-expression comparisons also indicate that immunoglobulin expression can be identified using mones in colon adenocarcinoma and that immune activity can be identified across multiple cancer types, and we verify these findings by expert histopathological review. Our work demonstrates that mones provide a morphological H&E decomposition that can be effectively associated with diverse phenotypes, analogous to the interpretability of transcription via gene expression values. Our work also demonstrates mones can be interpreted without using a classifier as a proxy
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
- …