We're thrilled to announce that HistoWiz will be attending not one, not two, but THREE events this week! In San Diego you can join us at the 10th Digital Pathology & AI Congress on May 7th-8th and the American Spatial Biology Congress on May 9th-10th. While in Baltimore, we’ll be attending the ASGCT 27th Annual Meeting from May 7th-11th. Get ready to explore the latest innovations in #DigitalPathology, #AI, and spatial biology with us. See you there! #DigiPathUSA #ASGCT2024 #HistoWiz #Innovation
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𝐄𝐦𝐛𝐚𝐫𝐤 𝐨𝐧 𝐚 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 𝐰𝐢𝐭𝐡 𝐘𝐨𝐮𝐫 𝐏𝐞𝐞𝐫𝐬 𝐚𝐭 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐁𝐢𝐨𝐥𝐨𝐠𝐲 𝟐𝟎𝟐𝟒! 🥼 On Day One, Track 2 will delve into 'Image Analysis, AI Powered Imaging & Digital Pathology for Spatial Biology,' featuring insights from leading experts including John Le Quesne, Ines Sequeira, Leo M. Carlin, Ilary Allodi, Cara Brodie, and Martin Fergie! 🧠 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮'𝐥𝐥 𝐮𝐧𝐜𝐨𝐯𝐞𝐫: 🌟 Accelerating the discovery of novel biomarkers and drug targets using spatial imaging 🌟 Tissue imaging and analysis using advanced spatial profiling techniques 🌟 AI guided technology for spatial analysis 🌟 Imaging data analysis / how to set a spatial experiment 🌟 Relevant spatial parameters in different model systems ➡️📖 Dive into the full track information: https://hubs.la/Q02gf8Zv0 Which aspect of spatial biology are you most excited to explore? Comment below! 👇 #OGOmics #SpatialBiologyUK24 #ImageAnalysis #DigitalPathology
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Automated Brain-Wide Identification of Microglia with The Translucence Teravoxel ToolKit (3TK) software. Pictured on the left, the images showcase Iba1 immunoreactivity in microglia. On the right, AI-powered 3D microglia labels are shown in a single 2D optical slice. Zoomed images show that the labels reveal detailed cellular morphology. These are 2D slices through 3D labels, and small seeming labels can be portions of microglia that project orthogonal to the 2D optical slice shown. Cell masks allow for counts, shape analysis, and fluorescence intensity measurements. The Translucence Teravoxel ToolKit (3TK) software then performs brain-wide regional quantification of independent object populations. AI-powered analytics provide a nuanced picture of microglia distribution, shape, and density– crucial for uncovering mechanisms associated with the development of various neurodegenerative diseases. Learn more about our AI-powered services and how to obtain precise cell identification and quantification. https://lnkd.in/gcfDpwy5 #TissueClearing, #3Dimaging, #Histology, #Microglia, #AI, #Quantification
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Join our "Sliding Into a Digital Cytology Workflow " Exhibitor Seminar at USCAP on Tuesday, March 26, at 12pm, Exhibitor Seminar Room 5. As #DigitalPathology gains ground in modern labs, many face the challenge of handling #cytology samples. While digital workflows benefit FFPE samples, optimizing cytology specimens with whole slide imaging and AI remains uncertain. #Coreplus will showcase their exploration of extended focal imaging, #WaterImmersion, and #AI, demonstrating improved workflow efficiency and results. Speakers: Marino de Socarraz: CEO, CorePlus; DPA Board Memeber; President, Precision Pathology Innovatoine Ben Cahoon: CEO, Techcyte Ryan Davis: Director Global Business, Digital Pathology and AI, #Epredia **Lunch will be provided for the first 50 attendees**
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Anatomic, Clinical and Cytopathologist with Molecular Expertise I Passionate About Humanity I Destined to Optimize Affordable Patient Outcome
It is always about #communication and how to improve it, especially in #pathology and all #healthcare! I am so glad Joe Lennerz is multilingual! #digitalpathology #AI #informatics
The Integration of Computational and Digital Pathology into the clinic is emerging. This represents a paradigm shift when compared to traditional modes of microscopic diagnosis. One key element in this transition periord is overcoming communication barriers between pathologists and developers... too often these barriers prevent realization of the full potential of the available solutions. In this paper, started within the Pathology Innovation Collaborative Community under the leadership of Steven Hart – he initially proposed to address the above gap between pathologists and developers. Challenges include Standardization, Availability of data and annotation, Cost, Quality concerns, Security, and Transparency. In contrast there are levels of supervision - size and diversity of datasets, image augmentation, ensemble learning, bagging, stacking, boosting, over vs. underfitting, and performance assessment approaches and various metrics that converge into clinical use cases. The team includes Qiangqiang Gy, Ankush Patel, Matthew Hanna, Chris Garcia, Mark Zarella, David McClintock and Steven Hart - and I believe we were able to provide a comprehensive outline for the understanding of computational and clinical paradigms regarding the translational gap. Link to pubmed: https://lnkd.in/epKCB8ni DOI: 10.5858/arpa.2023-0250-RA Link to Archives: https://lnkd.in/eCFaJJpE #Digitalpathology #WSI #computationalpathology #biomarker #precisiononcology #diagnostics #laboratory #AI #machinelearning #regulation #training #tuning #testing #generative #histology #histopathology #coding #laboratory
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The Integration of Computational and Digital Pathology into the clinic is emerging. This represents a paradigm shift when compared to traditional modes of microscopic diagnosis. One key element in this transition periord is overcoming communication barriers between pathologists and developers... too often these barriers prevent realization of the full potential of the available solutions. In this paper, started within the Pathology Innovation Collaborative Community under the leadership of Steven Hart – he initially proposed to address the above gap between pathologists and developers. Challenges include Standardization, Availability of data and annotation, Cost, Quality concerns, Security, and Transparency. In contrast there are levels of supervision - size and diversity of datasets, image augmentation, ensemble learning, bagging, stacking, boosting, over vs. underfitting, and performance assessment approaches and various metrics that converge into clinical use cases. The team includes Qiangqiang Gy, Ankush Patel, Matthew Hanna, Chris Garcia, Mark Zarella, David McClintock and Steven Hart - and I believe we were able to provide a comprehensive outline for the understanding of computational and clinical paradigms regarding the translational gap. Link to pubmed: https://lnkd.in/epKCB8ni DOI: 10.5858/arpa.2023-0250-RA Link to Archives: https://lnkd.in/eCFaJJpE #Digitalpathology #WSI #computationalpathology #biomarker #precisiononcology #diagnostics #laboratory #AI #machinelearning #regulation #training #tuning #testing #generative #histology #histopathology #coding #laboratory
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Another great Nature communications paper today ! This one describes a solution to build a correspondence between molecular-scale transcriptomics and tissue-scale atlases. The authors mapped tissue-scale mouse brain atlases to gene-based and cell-based transcriptomics data from #MERFISH and BARseq technologies and to histopathology and cross-species atlases to illustrate integration of diverse molecular and cellular datasets into a single coordinate system as a means of comparison and further atlas construction. For that work, the authors used serial #MERFISH sections from the Allen Institute which were produced under the BRAIN Initiative Cell Census Network (BICCN). Vizgen #MERSCOPE #MERFISH #SpatialTranscriptomics #SpatialBiology #SingleCell #SubCellular #OnlyRealDotsCount https://lnkd.in/e66zh6e5
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Cofounder & CEO at Segmed (YC W20) | Jr Deputy Editor at European Radiology | Former Stanford Radiology
📣 Check out our latest publication, led by Veit Sandfort! We describe and evaluate a #DeepLearning method for #denoising functional #cardiac CT #imaging, taking advantage of multiphase information in a 3D convolutional neural network. 👉 Our results show that a #3DUNet trained with #SyntheticData was highly effective in removing noise and artifacts from the high-noise portions of the cardiac cycle with reliable delineation of the left ventricle endocardial contour. The 3D U-Net outperformed conventional denoising methods like #BM3D. Receiving the full information of the cardiac cycle including low and high noise portions (as the 3D U-Net does) is key to allowing the #CNN to successfully remove severe noise and noise-related artifacts. 🌟 Read the paper here: https://lnkd.in/gwUCkU4U Veit Sandfort, Marina Codari, Domenico Mastrodicasa, Dominik Fleischmann, Stanford Radiology, Stanford University School of Medicine, Stanford Cardiovascular Imaging, Segmed, Inc., Tempus AI, University of Washington - Department of Radiology
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Our U.S. colleagues enjoyed demonstrating our solutions at this year’s Annual Clinical Genetics Meeting held by the American College of Medical Genetics and Genomics. The meeting took place in Toronto, Canada, and brought together leaders and prominent figures in the field of medical genetics and genomics, including clinical research. We hope you had a chance to meet Peter Hartmayer and Jeff Sanford. Selected highlights of karyogram creation with Ikaros include: 🎯 Accommodate a wide range of banding techniques currently in use, 🎯 Utilize diverse specimens, such as amniotic fluid, peripheral blood, chorionic villus, bone marrow, and tissue, without limitations based on specific diseases, for banding analysis, 🎯 Incorporate multiple features to enhance the interpretation of metaphases and streamline the karyotyping process, 🎯 Leverage Deep Neural Networks (DNN) to assist in the separation and assignment of chromosomes. 🎯 Maintain a continuous log of processing steps and enjoy unrestricted access to the original images, 🎯 Facilitate seamless transitions between different capture settings, allowing for easy shifts from brightfield to fluorescence and vice versa, 🎯 Opt for manual image acquisition with one-click capture, automatic contrast enhancement, and the selection of the optimal focus plane. Want to know more about our products and services? Contact us through our website ➡️ https://lnkd.in/d-wZ4VTu #MetaSystems #AutomatedImaging #Ikaros #Metafer #ACGM #ACGM2024
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How is #AI improving imaging in #healthcare? At the European Signal Processing Conference (EUSIPCO) 2023, our researchers ANDREW GIGIE, Krishna Kanth Rokkam, A Anil Kumar, Dr. Tapas Chakravarty, Anwesha Khasnobish, and Arpan Pal presented an enhanced microwave imaging technique that can efficiently detect breast tumours. This technique uses a model-based reconstruction algorithm and a reinforcement learning-based intelligent scanning mechanism. Read the #ResearchPaper here- https://bit.ly/3HmNMMT #Research #Publications #BreastCancer
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