The use of deep learning state-of-the-art architectures for oral epithelial dysplasia grading: A comparative appraisal.

BACKGROUND: Dysplasia grading systems for oral epithelial dysplasia are a source of disagreement among pathologists. Therefore, machine learning approaches are being developed to mitigate this issue. METHODS: This cross-sectional study included a cohort of 82 patients with oral potentially malignant disorders and correspondent 98 hematoxylin and eosin-stained whole slide images with biopsied-proven dysplasia. All whole-slide images were manually annotated based on the binary system for oral epithelial dysplasia. The annotated regions of interest were segmented and fragmented into small patches and non-randomly sampled into training/validation and test subsets. The training/validation data were color augmented, resulting in a total of 81,786 patches for training. The held-out independent test set enrolled a total of 4,486 patches. Seven state-of-the-art convolutional neural networks were trained, validated, and tested with the same dataset. RESULTS: The models presented a high learning rate, yet very low generalization potential. At the model development, VGG16 performed the best, but with massive overfitting. In the test set, VGG16 presented the best accuracy, sensitivity, specificity, and area under the curve (62%, 62%, 66%, and 65%, respectively), associated with the higher loss among all Convolutional Neural Networks (CNNs) tested. EfficientB0 has comparable metrics and the lowest loss among all convolutional neural networks, being a great candidate for further studies. CONCLUSION: The models were not able to generalize enough to be applied in real-life datasets due to an overlapping of features between the two classes (i.e., high risk and low risk of malignization).

Influence of Data Augmentation Strategies on the Segmentation of Oral Histological Images Using Fully Convolutional Neural Networks.

Segmentation of tumor regions in H &E-stained slides is an important task for a pathologist while diagnosing different types of cancer, including oral squamous cell carcinoma (OSCC). Histological image segmentation is often constrained by the availability of labeled training data since labeling histological images is a highly skilled, complex, and time-consuming task. Thus, data augmentation strategies become essential to train convolutional neural networks models to overcome the overfitting problem when only a few training samples are available. This paper proposes a new data augmentation strategy, named Random Composition Augmentation (RCAug), to train fully convolutional networks (FCN) to segment OSCC tumor regions in H &E-stained histological images. Given the input image and their corresponding label, a pipeline with a random composition of geometric, distortion, color transfer, and generative image transformations is executed on the fly. Experimental evaluations were performed using an FCN-based method to segment OSCC regions through a set of different data augmentation transformations. By using RCAug, we improved the FCN-based segmentation method from 0.51 to 0.81 of intersection-over-union (IOU) in a whole slide image dataset and from 0.65 to 0.69 of IOU in a tissue microarray images dataset.

Artificial intelligence as a tool for diagnosis in digital pathology whole slide images: A systematic review.

Digital pathology had a recent growth, stimulated by the implementation of digital whole slide images (WSIs) in clinical practice, and the pathology field faces shortage of pathologists in the last few years. This scenario created fronts of research applying artificial intelligence (AI) to help pathologists. One of them is the automated diagnosis, helping in the clinical decision support, increasing efficiency and quality of diagnosis. However, the complexity nature of the WSIs requires special treatments to create a reliable AI model for diagnosis. Therefore, we systematically reviewed the literature to analyze and discuss all the methods and results in AI in digital pathology performed in WSIs on H&E stain, investigating the capacity of AI as a diagnostic support tool for the pathologist in the routine real-world scenario. This review analyzes 26 studies, reporting in detail all the best methods to apply AI as a diagnostic tool, as well as the main limitations, and suggests new ideas to improve the AI field in digital pathology as a whole. We hope that this study could lead to a better use of AI as a diagnostic tool in pathology, helping future researchers in the development of new studies and projects.

A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers.

The treatment and management of early stage estrogen receptor positive (ER+) breast cancer is hindered by the difficulty in identifying patients who require adjuvant chemotherapy in contrast to those that will respond to hormonal therapy. To distinguish between the more and less aggressive breast tumors, which is a fundamental criterion for the selection of an appropriate treatment plan, Oncotype DX (ODX) and other gene expression tests are typically employed. While informative, these gene expression tests are expensive, tissue destructive, and require specialized facilities. Bloom-Richardson (BR) grade, the common scheme employed in breast cancer grading, has been shown to be correlated with the Oncotype DX risk score. Unfortunately, studies have also shown that the BR grade determined experiences notable inter-observer variability. One of the constituent categories in BR grading is the mitotic index. The goal of this study was to develop a deep learning (DL) classifier to identify mitotic figures from whole slides images of ER+ breast cancer, the hypothesis being that the number of mitoses identified by the DL classifier would correlate with the corresponding Oncotype DX risk categories. The mitosis detector yielded an average F-score of 0.556 in the AMIDA mitosis dataset using a 6-fold validation setup. For a cohort of 174 whole slide images with early stage ER+ breast cancer for which the corresponding Oncotype DX score was available, the distributions of the number of mitoses identified by the DL classifier was found to be significantly different between the high vs low Oncotype DX risk groups (P < 0.01). Comparisons of other risk groups, using both ODX score and histological grade, were also found to present significantly different automated mitoses distributions. Additionally, a support vector machine classifier trained to separate low/high Oncotype DX risk categories using the mitotic count determined by the DL classifier yielded a 83.19% classification accuracy. © 2017 International Society for Advancement of Cytometry.

An adaptable navigation strategy for Virtual Microscopy from mobile platforms.

Real integration of Virtual Microscopy with the pathologist service workflow requires the design of adaptable strategies for any hospital service to interact with a set of Whole Slide Images. Nowadays, mobile devices have the actual potential of supporting an online pervasive network of specialists working together. However, such devices are still very limited. This article introduces a novel highly adaptable strategy for streaming and visualizing WSI from mobile devices. The presented approach effectively exploits and extends the granularity of the JPEG2000 standard and integrates it with different strategies to achieve a lossless, loosely-coupled, decoder and platform independent implementation, adaptable to any interaction model. The performance was evaluated by two expert pathologists interacting with a set of 20 virtual slides. The method efficiently uses the available device resources: the memory usage did not exceed a 7% of the device capacity while the decoding times were smaller than the 200 ms per Region of Interest, i.e., a window of 256×256 pixels. This model is easily adaptable to other medical imaging scenarios.

Diagnosing Pathological Prognostic Factors in Retinoblastoma: Correlation between Traditional Microscopy and Digital Slides.

OBJECTIVE: It was the aim of this study to determine the diagnostic accuracy of high-risk prognostic factors and morphological characteristics of retinoblastomas using digital whole slide images (WSI) generated by a scanner. METHODS: Forty-seven H&E sections of glass slides with high-risk morphological features of retinoblastoma were analyzed. Slides were scanned as WSI and reviewed. The results were compared with those obtained after reviewing the slides using a regular microscope as the gold standard. McNemar's test (MT), the percentage of agreement (POA), and sensitivity (S) and specificity (Sp) were evaluated between WSI and conventional microscopy. RESULTS: There were no differences with respect to multicentricity, growth type, rosette formation, choroidal invasion, anterior chamber invasion, extraocular extension, scleral extension, optic nerve invasion, necrosis, or Azzopardi effect between WSI analysis and light microscopy (MT, p = 1.0; POA = 100%; S = 100%, and Sp = 100%). Discordance was found in 1 case where calcification could not be found using WSI (MT, p = 1.00; POA = 97.9%; S = 100%, and Sp = 97.8%). CONCLUSION: To the best of our knowledge, this is the first report using digital pathology (WSI) to evaluate prognostic factors in eyes containing retinoblastomas. Using WSI, the pathologist was able to detect high-risk morphological features in retinoblastoma. To date, WSI is an important tool, in particular for ophthalmic pathologists examining enucleation and exenteration specimens.