Canon Medical Systems Corporation’s Post

🌟 Exciting News! Check out our latest blog post on enhancing digital hologram reconstruction using reverse-attention loss for untrained physics-driven deep learning models with uncertain distance. This pioneering approach addresses the Autofocusing challenge in untrained deep-learning methods, offering both theoretical analysis and experimental superiority in efficiency and accuracy. See the full article here: https://bit.ly/49WQPs7 #DigitalHologram #DeepLearning #PhysicsDrivenModels

🔍 Exciting Update! Just released a new blog post on "DINOv2 based Self Supervised Learning For Few Shot Medical Image Segmentation". Deep learning models are pivotal in medical image segmentation, and the challenge of adaptability to unforeseen categories is addressed through Few-shot segmentation (FSS) in this post. Learn about the potential of using DINOv2 features in conjunction with ALPNet for enhanced performance and robust medical image analysis. Read the full post here: https://bit.ly/4c7FOWq #MedicalImageSegmentation #DINOv2 #ALPNet #DeepLearning #ComputerVision

📢 Call for reading a new paper publication titled “Automated Weld Defect Detection in Industrial Ultrasonic B-Scan Images Using Deep Learning” https://lnkd.in/eqtptH6W Author: Amir-M. Naddaf-Sh,Vinay S. Baburao and Hassan Zargarzadeh * #ndt #ultrasound #welddefectdetection #deeplearning

This is a tremendous series that delves deeper into the debate of how #AI is changing much of what we do, and we were glad to be a part of it. Thank you again to Dataiku for the fabulous work here. Check it out to see some of the most thoughtful work going on in the world of #artificialintelligence

Just encountered "MindEye2," an incredible leap in AI-assisted brain activity reconstruction, that I had to share with you all! 🧠🎨 This innovative study drastically cuts down the need for extensive fMRI data—from numerous hours to just one—through AI-driven models. By using shared-subject data and advanced mapping to reconstruct visual perceptions, it achieves detailed images from minimal data. 🚀 This opens new doors to understanding the brain's visual processes more efficiently. Curious minds can find all the code on GitHub. Welcome to a future where a single MRI visit might unlock the secrets of our visual thoughts through AI! 🌐💡 #BrainActivity #MindEye2 #AIInnovation

You might be hearing a lot about AutoNAC, the neural architecture search engine responsible for creating state-of-the-art generative AI and computer vision models such as YOLO-NAS, DeciLM, DeciDiffusion, DeciCoder, YOLO-NAS Pose, and YOLO-NAS-Sat. But what is AutoNAC and how does it work? Eitan Fredman Ganeles provides an overview. 👇 Learn more here: https://lnkd.in/gwUvZTgT #deeplearning #computervision #neuralnetworks #generativeai #llms

Discover the future of digital twins with Ansys TwinAI! Register for this upcoming webinar and learn how we blend physics models with AI insights for unparalleled accuracy. https://bit.ly/4du4CrT

🔍 Delighted to have shared insights at the Optical Fiber Communication Conference in San Diego this week. My presentation centered around the usage of variational autoencoders and generative adversarial networks, where we explored how this technology can enable soft-failure identification in optical networks on low amounts of available training data. 🙏 Huge thanks for all the kind attention in the room during my talk and the TPC for the opportunity to present our latest research. Of course also thanks to my coauthors Sebastian Kühl , Annika Dochhan and Stephan Pachnicke. Looking forward to see how generative AI and machine learning will change the operation of optical networks! #OFC2024 #opticalnetworks #machinelearning

Discover the future of digital twins with Ansys TwinAI! Register for this upcoming webinar and learn how we blend physics models with AI insights for unparalleled accuracy. https://bit.ly/4ctoTfU