Radformation’s Post

Correlative light electron #microscopy is a powerful technique that can provide contextual analysis of complex biological systems and disorders. Read this application note to learn how a research team at EMBL deconvoluted the process of cancer metastasis using different imaging modalities and Thermo Scientific’s Amira Software

Correlative light electron #microscopy is a powerful technique that can provide contextual analysis of complex biological systems and disorders. Read this application note to learn how a research team at EMBL deconvoluted the process of cancer metastasis using different imaging modalities and Thermo Scientific’s Amira Software

Correlative light electron #microscopy is a powerful technique that can provide contextual analysis of complex biological systems and disorders. Read this application note to learn how a research team at EMBL deconvoluted the process of cancer metastasis using different imaging modalities and Thermo Scientific’s Amira Software

Glad to share that our paper that explored the potentials of extreme learning algorithms and Deep Feed -Forward Networks for Diagnosis of Breast cancer'' has been accepted in Biomedical Engineering and Computational Biology published by Sage indexed in Clarivate Analytics

A spectroscopic approach to extracellular vesicle identification: Mathias Jensen, Olav Gaute Hellesø and collaborators at UiT- The Arctic University of Norway developed a deep learning architecture based on Raman spectra of EVs and lipoproteins from 13 biological sources and from two laboratories https://lnkd.in/exDwV8t4 Their model distinguished between EVs from activated vs. non-activated blood platelets and EVs/lipoproteins from prostate cancer patients vs. non-cancer controls using label-free differentiation. It employed a neural network classifier to sort spectra based on their sample origin, achieving 92.2% sensitivity and 92.3% selectivity across 769 measurements performed by two labs using different setups. An article also authored by Eduarda M. Guerreiro, Agustin E., Sergei G. Kruglik, Cees Otto, Omri Snir and Benjamin Ricaud #extracellularvesicles #exosomes #ramanspectroscopy #PCA #Vesiculab

Please check this latest article out, done with our colleagues Drs Orest Boyko and Laurence Court Revenue Cycle Coding Strategies

I'm thrilled to announce that our latest research paper, "Development of message passing-based graph convolutional networks for classifying cancer pathology reports", has been published in BMC Medical Informatics and Decision Making as part of Springer Nature! 🎉 This collaborative work, lead by Hong-Jun Yoon explores innovative approaches using graph convolutional networks to enhance the classification of cancer pathology reports. You can read the full article here: https://lnkd.in/epJEb3Nm Many thanks to everyone who supported this effort. We are excited to contribute to advancements in medical informatics and look forward to the impact this work will have on the field! #AI #MachineLearning #MedicalInformatics #CancerResearch #GraphConvolutionalNetworks #DataScience #Research

In in a thought-provoking dialogue published in the latest edition of the ACE newsletter, Ron DiGiaimo, CEO of Revenue Cycle Coding Strategies, and Bri Driggers, Content Marketing Manager, engage with Dr. Laurence Court, professor in the Department of Radiation Physics, Division of Radiation Oncology at The University of Texas MD Anderson Cancer Center and Dr. Orest Boyko, Diagnostic Radiologist and Chief Scientific Officer for the American Board of Artificial Intelligence in Healthcare. Both stand at the forefront of integrating AI within their realms and speak on how AI is affecting their spheres of influence. Link below to find: 📰"Redefining Diagnostic Imaging & Cancer Care: The Promise of AI Developments According to Industry Leaders." 📲 https://bit.ly/4bzMYBH . #AI #oncology #technology #innovation #healthcare #artificialintelligence #precision #efficiency #leadership #continuingeducation #knowledgesharing #AdvancingCancerCare #radiology #personalizemedicine

I am excited to share one more research of our team that is now available in IEEE Xplore Digital Library which is "Medical Image Analysis of Multiple Myeloma Diagnosis using CNN and KNN based Approach." The main objective of the research is to propose a framework for classifying bone marrow cancer cells, specifically focusing on the diagnosis of multiple myeloma. The study aims to evaluate the efficacy of this proposed framework using advanced medical image analysis techniques, particularly employing a CNN-KNN model. We utilized the Median filter on the already-processed data and common segmentation methods such as cell segmentation and the separation of nucleus and cytoplasm. The ultimate goal was to improve the accuracy of diagnosis, evaluation, and prognosis of multiple myeloma, with the proposed framework achieving an accuracy of 98.47%, outperforming standard methods like CNN and KNN. The study also emphasizes the increasing application of medical image analysis and the importance of functional imaging in presenting sick tissue and differentiating it from the normal body milieu. Additionally, we discussed the regional and national variances in patient outcomes due to differences in biological features, presentation stage, and access to innovative treatments in the context of multiple myeloma. The resource is available at: Google Scholar: https://lnkd.in/gZFanUsh IEEE Xplore: https://lnkd.in/g8_9fRes #ieee #ieeeconference #ieeexplore #biomedicalengineering #electricalengineering #electricaldesign #biomedical

📣 Exciting news! Our latest paper on the impact of reduced [18F]FDG doses in whole-body PET/CT imaging has been published and is now accessible in the journal Frontiers in Physics. Take a look and share your insights with us 💡