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Whole Slide Imaging (WSI) in Pathology: Automated Stitching of Microscope Slides Using Python OpenCV and Video Assets

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Digital pathology

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Automated stitching of microscope slides for Whole Slide Imaging (WSI) in pathology using Python OpenCV and video assets is a promising area of research. Digital pathology advancements allow pathologists to interpret slides digitally, enhancing collaboration and research. Additionally, AI-based tools like SlideQC aid in automating quality control by detecting artifacts in WSI, streamlining workflows. Challenges like high data storage requirements for WSIs have led to the exploration of compression techniques like lossy JPEG compression and VQVAE2 models to reduce file sizes while maintaining essential features. Label-free histology platforms offer an alternative to traditional staining methods, providing high-resolution images compatible with digital pathology tools. Integrating these technologies can enhance diagnostic efficiency and broaden the scope of stains applied in pathology practices.

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Open access•Posted Content•DOI Automated Whole Slide Imaging for Label-Free Histology using Photon Absorption Remote Sensing Microscopy 26 Apr 2023	Not addressed in the paper.
Journal Article•DOI Abstract 5442: SlideQC: An AI-based tool for automated quality control of whole-slide digital pathology images Stefan Reinhard, Inti Zlobec, Peter D. Caie - Show less +2 more 04 Apr 2023-Cancer Research	Not addressed in the paper.
Journal Article•DOI Whole Slide Imaging Technology and Its Applications: Current and Emerging Perspectives. Ekta Jain, Ankush Patel, Anil Parwani, Saba Shafi, Shivani Sharma, Sambit K. Mohanty - Show less +5 more 12 Jul 2023-International Journal of Surgical Pathology	Not addressed in the paper.
Open access•Journal Article•DOI Whole slide imaging (WSI) scanner differences influence optical and computed properties of digitized prostate cancer histology Savannah Duenweg, Samuel Bobholz, Allison Lowman, Aleksandra Winiarz, Fitzgerald Kyereme, Kenneth A. Iczkowski, Peter S. LaViolette - Show less +6 more 01 Jul 2023-Journal of pathology informatics	Not addressed in the paper.
Open access•Proceedings Article•DOI Digital pathology whole slide image compression with vector quantized variational autoencoders Jason Keighley, Marc de Kamps, A Wright, Darren Treanor - Show less +3 more 06 Apr 2023	Not addressed in the paper.

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What is the most good contribution using generative AI in pathology?5 answersThe most significant contribution of generative AI in pathology lies in the generation of synthetic histological images to address the challenge of limited training data, enhancing diagnostic accuracy and treatment outcomes. By utilizing Generative Adversarial Networks (GANs), researchers have been able to create high-quality tissue images for various organs like colon and prostate, achieving indistinguishable synthetic samples from real ones. These synthetic images not only aid in debiasing datasets but also allow for the control of features within them, improving the generalizability of algorithms trained on them. Furthermore, the integration of generative AI models like PathAsst can revolutionize diagnostic and predictive analytics in pathology, enhancing diagnostic capabilities and treatment processes.

What ist the current state of the art in digital pathology?4 answersDigital pathology is an evolving field that combines information technology, artificial intelligence, and histopathology to improve diagnostic capabilities and analysis of tissue samples. The current state of the art in digital pathology involves the use of advanced algorithms and computer-assisted diagnostic techniques to analyze digital histopathological images with higher consistency and precision than optical microscopy. Computer-aided image analysis has the potential to identify, extract, and quantify features in more detail compared to human pathologists, leading to the development of new diagnostic algorithms and prediction models. Additionally, the application of artificial intelligence, particularly convolutional neural networks (CNNs), is being explored for automatic classification of scanned microscopic slides in digital pathology. New technologies, such as label-free infrared imaging, are also being developed to enhance tissue analysis beyond conventional histopathology. These advancements in digital pathology have the potential to improve clinical workflow, patient management, and the understanding of various lung diseases.

How can Ilastik be used for digital pathology?5 answersIlastik, a machine learning software, can be used for digital pathology in various ways. It can be utilized for diagnostic screening, morphometric analysis of biomarkers, and improving diagnostic efficiency. Additionally, Ilastik can be used to develop pathological artificial intelligence (AI) models by applying AI algorithms such as machine learning and deep learning for the detection, segmentation, registration, processing, and classification of digitized pathological images. The software can also contribute to the generation of pathological big data and the discovery of new meanings of prognosis and therapeutic response in pathological images. Furthermore, Ilastik can assist in the standardization and color normalization of images, which are crucial for developing successful pathological AI models.

What are algorithms used in digital pathology?5 answersAlgorithms used in digital pathology include artificial intelligence (AI) techniques such as machine learning and deep learning. These algorithms are used for various tasks such as detection, segmentation, registration, processing, and classification of digitized pathological images. They can be utilized for diagnostic screening, morphometric analysis of biomarkers, and the discovery of new meanings in pathological images. Specifically, deep learning algorithms, such as fully convolutional networks, have shown promise in pathology image segmentation, allowing for accurate and efficient analysis of whole slide images. The integration of digital images into pathology, combined with advanced algorithms, extends the boundaries of the pathologist's vision and allows for the development of new diagnostic algorithms and prediction models. These algorithms have the potential to identify new characteristics of diseases based on changes at the cellular and molecular level.

Whole slide imaging predictions for Diffuse B-cell Lymphomas5 answersWhole-slide imaging predictions for diffuse B-cell lymphomas have not been specifically addressed in the abstracts provided.

How do I study clinical pathology?5 answers

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