Results 31 to 40 of about 43,201,948 (340)
npj Digital Medicine, 2020 The emergence of digital pathology has opened new horizons for histopathology. Artificial intelligence (AI) algorithms are able to operate on digitized slides to assist pathologists with different tasks.
Shivam Kalra +10 more
doaj +1 more source
Virchow: A Million-Slide Digital Pathology Foundation Model [PDF]
arXiv.org, 2023 The use of artificial intelligence to enable precision medicine and decision support systems through the analysis of pathology images has the potential to revolutionize the diagnosis and treatment of cancer.
E. Vorontsov +26 more
semanticscholar +1 more source
Banff Digital Pathology Working Group: Going digital in transplant pathology [PDF]
American Journal of Transplantation, 2020 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 ...
Alton B. Farris +12 more
openaire +7 more sources
Digital Pathology: An Overview
, 2023 Digital pathology (DP) is a sub-field of pathology that focuses on data management based on information generated from digitized specimen slides.
1P. D. Baraiya, 2J. M. Patel, 3P. D. Vihol, 4J.K. Raval, 5H.C. Parmar, 6S. A. Patel, 7P. R. Panzade
core +1 more source
Structured State Space Models for Multiple Instance Learning in Digital Pathology [PDF]
International Conference on Medical Image Computing and Computer-Assisted Intervention, 2023 Multiple instance learning is an ideal mode of analysis for histopathology data, where vast whole slide images are typically annotated with a single global label.
Leo Fillioux +4 more
semanticscholar +1 more source
, 2022 Complete digital pathology transformation for primary histopathological diagnosis is a challenging yet rewarding endeavor. Its advantages are clear with more efficient workflows, but there are many technical and functional difficulties to be faced.
Josep Castellví Vives +35 more
core +1 more source
SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology [PDF]
ISIC/Care-AI/MedAGI/DeCaF@MICCAI, 2023 Semantic segmentations of pathological entities have crucial clinical value in computational pathology workflows. Foundation models, such as the Segment Anything Model (SAM), have been recently proposed for universal use in segmentation tasks.
Jing-Wei Zhang +6 more
semanticscholar +1 more source
Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potential [PDF]
Comput. Medical Imaging Graph., 2022 Digital whole slides images contain an enormous amount of information providing a strong motivation for the development of automated image analysis tools. Particularly deep neural networks show high potential with respect to various tasks in the field of
M. Gadermayr, M. Tschuchnig
semanticscholar +1 more source
Pathology Education Powered by Virtual and Digital Transformation: Now and the Future
, 2022 Context.— Myriad forces are changing teaching and learning strategies throughout all stages and types of pathology education. Pathology educators and learners face the challenge of adapting to and adopting new methods
Ho, Jonhan +41 more
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Journal of Pathology Informatics, 2023 Whole slide imaging is revolutionizing the field of pathology and is currently being used for clinical, educational, and research initiatives by an increasing number of institutions. Pathology departments have distinct needs for digital pathology systems,
Orly Ardon +15 more
doaj +1 more source