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

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

KimiaNet: Training a Deep Network for Histopathology using High-Cellularity

With the recent progress in deep learning, one of the common approaches to represent images is extracting deep features. A primitive way to do this is by using off-the-shelf models. However, these features could be improved through fine-tuning or even training a network from scratch by domain-specific images. This desirable task is hindered by the lack of annotated or labeled images in the field of histopathology. In this thesis, a new network, namely KimiaNet, is proposed that uses an existing dense topology but is tailored for generating informative and discriminative deep features from histopathology images for image representation. This model is trained based on the existing DenseNet-121 architecture but by using more than 240,000 image patches of 1000 ⨉ 1000 pixels acquired at 20⨉ magnification. Considering the high cost of histopathology image annotation, which makes the idea impractical at a large scale, a high-cellularity mosaic approach is suggested which could be used as a weak or soft labeling method. Patches used for training the KimiaNet are extracted from 7,126 whole slide images of formalin-fixed paraffin-embedded (FFPE) biopsy samples, spanning 30 cancer sub-types and publicly available through The Cancer Genome Atlas (TCGA) repository. The quality of features generated by KimiaNet are tested via two types of image search, (i) given a query slide, searching among all of the slides and finding the ones with the tissue type similar to the query’s and (ii) searching among slides within the query slide’s tumor type and finding slides with the same cancer sub-type as the query slide’s. Compared to the pre-trained DenseNet-121 and the fine-tuned versions, KimiaNet achieved predominantly the best results for both search modes. In order to get an intuition of how effective training from scratch is on the expressiveness of the deep features, the deep features of randomly selected patches, from each cancer subtype, are extracted using both KimiaNet and pre-trained DenseNet-121 and visualized after reducing their dimensionality using t-distributed Stochastic Neighbor Embedding (tSNE). This visualization illustrates that for KimiaNet, the instances of each class can easily be distinguished from others while for pre-trained DenseNet the instances of almost all of the classes are mixed together. This comparison is another verification to show that how discriminative training with domain-specific images has made the features. Also, four simpler networks, made up of repetitions of convolutional, batch-normalization and Rectified Linear Unit (ReLU) layers, (CBR networks) are implemented and compared against the KimiaNet to check if the network design could still be further simplified. The experiments demonstrated that KimiaNet features are by far better than CBR networks which validate the DenseNet-121 as a good candidate for KimiaNet’s architecture

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