Banff Digital Pathology Working Group: Going digital in transplant pathology.

The Banff Digital Pathology Working Group (DPWG) was formed in the time leading up to and during the joint American Society for Histocompatibility and Immunogenetics/Banff Meeting, September 23-27, 2019, held in Pittsburgh, Pennsylvania. At the meeting, the 14th Banff Conference, presentations directly and peripherally related to the topic of "digital pathology" were presented; and discussions before, during, and after the meeting have resulted in a list of issues to address for the DPWG. Included are practice standardization, integrative approaches for study classification, scoring of histologic parameters (eg, interstitial fibrosis and tubular atrophy and inflammation), algorithm classification, and precision diagnosis (eg, molecular pathways and therapeutics). Since the meeting, a survey with international participation of mostly pathologists (81%) was conducted, showing that whole slide imaging is available at the majority of centers (71%) but that artificial intelligence (AI)/machine learning was only used in ≈12% of centers, with a wide variety of programs/algorithms employed. Digitalization is not just an end in itself. It also is a necessary precondition for AI and other approaches. Discussions at the meeting and the survey highlight the unmet need for a Banff DPWG and point the way toward future contributions that can be made

Aging display's effect on interpretation of digital pathology slides

It is our conjecture that the variability of colors in a pathology image effects the interpretation of pathology cases, whether it is diagnostic accuracy, diagnostic confidence, or workflow efficiency. In this paper, digital pathology images are analyzed to quantify the perceived difference in color that occurs due to display aging, in particular a change in the maximum luminance, white point, and color gamut. The digital pathology images studied include diagnostically important features, such as the conspicuity of nuclei. Three different display aging models are applied to images: aging of luminance & chrominance, aging of chrominance only, and a stabilized luminance & chrominance (i.e., no aging). These display models and images are then used to compare conspicuity of nuclei using CIE deltaE2000, a perceptual color difference metric. The effect of display aging using these display models and images is further analyzed through a human reader study designed to quantify the effects from a clinical perspective. Results from our reader study indicate significant impact of aged displays on workflow as well as diagnosis as follow. As compared to the originals (no-aging), slides with the effect of aging simulated were significantly more difficult to read (p-value of 0.0005) and took longer to score (p-value of 0.02). Moreover, luminance+chrominance aging significantly reduced inter-session percent agreement of diagnostic scores (p-value of 0.0418)

Expanding application of digital pathology in Japan - from education, telepathology to autodiagnosis

<p>Abstract</p> <p>Background</p> <p>Digital pathology, i.e., applications of digital information technologies to pathology practice, has been expanding in the recent decades and the mode of pathology diagnostic practice is changing with enhanced precision. In the present study the changing processes of digital pathology in Japan were investigated and trends to future were discussed.</p> <p>Methods</p> <p>The changing status of digital pathology was investigated through reviewing the records of annual meetings of the Japanese Research Society of Telepathology and Pathology Informatics (JRST-PI) and of the Japanese pathology related medical and informatics journals. The results of the Japanese questionnaire survey conducted in 2008-2009 on telepathology and virtual slide were also reviewed. In addition effectiveness of an experimental automatic pathology diagnostic aid system using computer artificial intelligence was investigated by checking its rate of correct diagnosis for given prostate carcinoma digital images.</p> <p>Results</p> <p>Telepathology played a central role in the development of digital pathology in Japan. Both macroscopic and microscopic pathology digital images were routinely generated and used for diagnostic purposes in major hospitals. Virtual slide (VS) digital images were used first for education then for conference, consultation and also gradually for routine diagnosis and telepathology. The experimental automatic diagnostic aid system achieved the rate of correct diagnosis around 95% for prostate carcinoma and its use for automatic mapping of cancerous areas in a given tissue image was successful.</p> <p>Conclusions</p> <p>Advance in the digital information technologies gave revolutionary impacts on pathology education, conference, consultation, diagnosis, telepathology and also on pathology diagnostic procedures in Japan. The future will be bright for pathologists by the advanced digital pathology but we should pay attention to make the technologies and their effects under our control.</p

Classification and Retrieval of Digital Pathology Scans: A New Dataset

In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image classification and retrieval in digital pathology. We use the whole scan images of 24 different tissue textures to generate 1,325 test patches of size 1000$\times$1000 (0.5mm$\times$0.5mm). Training data can be generated according to preferences of algorithm designer and can range from approximately 27,000 to over 50,000 patches if the preset parameters are adopted. We propose a compound patch-and-scan accuracy measurement that makes achieving high accuracies quite challenging. In addition, we set the benchmarking line by applying LBP, dictionary approach and convolutional neural nets (CNNs) and report their results. The highest accuracy was 41.80\% for CNN.Comment: Accepted for presentation at Workshop for Computer Vision for Microscopy Image Analysis (CVMI 2017) @ CVPR 2017, Honolulu, Hawai

A Review on the Applications of Crowdsourcing in Human Pathology

The advent of the digital pathology has introduced new avenues of diagnostic medicine. Among them, crowdsourcing has attracted researchers' attention in the recent years, allowing them to engage thousands of untrained individuals in research and diagnosis. While there exist several articles in this regard, prior works have not collectively documented them. We, therefore, aim to review the applications of crowdsourcing in human pathology in a semi-systematic manner. We firstly, introduce a novel method to do a systematic search of the literature. Utilizing this method, we, then, collect hundreds of articles and screen them against a pre-defined set of criteria. Furthermore, we crowdsource part of the screening process, to examine another potential application of crowdsourcing. Finally, we review the selected articles and characterize the prior uses of crowdsourcing in pathology

PathologyGAN: Learning deep representations of cancer tissue

We apply Generative Adversarial Networks (GANs) to the domain of digital pathology. Current machine learning research for digital pathology focuses on diagnosis, but we suggest a different approach and advocate that generative models could drive forward the understanding of morphological characteristics of cancer tissue. In this paper, we develop a framework which allows GANs to capture key tissue features and uses these characteristics to give structure to its latent space. To this end, we trained our model on 249K H&E breast cancer tissue images, extracted from 576 TMA images of patients from the Netherlands Cancer Institute (NKI) and Vancouver General Hospital (VGH) cohorts. We show that our model generates high quality images, with a Frechet Inception Distance (FID) of 16.65. We further assess the quality of the images with cancer tissue characteristics (e.g. count of cancer, lymphocytes, or stromal cells), using quantitative information to calculate the FID and showing consistent performance of 9.86. Additionally, the latent space of our model shows an interpretable structure and allows semantic vector operations that translate into tissue feature transformations. Furthermore, ratings from two expert pathologists found no significant difference between our generated tissue images from real ones. The code, generated images, and pretrained model are available at https://github.com/AdalbertoCq/Pathology-GANComment: MIDL 2020 final versio

Digital pathology access and usage in the UK: results from a national survey on behalf of the National Cancer Research Institute's CM-Path initiative.

Aim To canvass the UK pathology community to ascertain current levels of digital pathology usage in clinical and academic histopathology departments, and prevalent attitudes to digital pathology. Methods A 15-item survey was circulated to National Health Service and academic pathology departments across the UK using the SurveyMonkey online survey tool. Responses were sought at a departmental or institutional level. Where possible, departmental heads were approached and asked to complete the survey, or forward it to the most relevant individual in their department. Data were collected over a 6-month period from February to July 2017. Results 41 institutes from across the UK responded to the survey. 60% (23/39) of institutions had access to a digital pathology scanner, and 60% (24/40) had access to a digital pathology workstation. The most popular applications of digital pathology in current use were undergraduate and postgraduate teaching, research and quality assurance. Investigating the deployment of digital pathology in their department was identified as a high or highest priority by 58.5% of institutions, with improvements in efficiency, turnaround times, reporting times and collaboration in their institution anticipated by the respondents. Access to funding for initial hardware, software and staff outlay, pathologist training and guidance from the Royal College of Pathologists were identified as factors that could enable respondent institutions to increase their digital pathology usage. Conclusion Interest in digital pathology adoption in the UK is high, with usage likely to increase in the coming years. In light of this, pathologists are seeking more guidance on safe usage