DeepLab’s Post

🤔 Trivia Question: In advanced AI-driven patient monitoring systems, which specific technology enables the prediction of deterioration in a patient’s condition by continuously analyzing real-time data streams and integrating heterogeneous data sources from various medical devices? A) Convolutional Neural Networks (CNNs)   B) Long Short-Term Memory Networks (LSTMs)   C) Reinforcement Learning   D) Federated Learning  #AI #Healthcare #Technology #MachineLearning #PatientCare Sciqst, Precise References in Medicine. www.sciqst.com

I'm excited to share a significant milestone in my journey with deep learning and medical imaging! 🚀 I recently developed a custom Convolutional Neural Network (CNN) architecture that achieved an impressive 91% accuracy in classifying chest X-ray images. This model focuses on detecting three critical diseases: atelectasis, cardiomegaly, and effusion. The high accuracy not only demonstrates the potential of machine learning in aiding diagnostic processes but also reinforces the transformative role of AI in healthcare. The project was both challenging and rewarding, involving meticulous data preprocessing, model tuning, and validation to ensure robust performance across diverse datasets. I believe that integrating advanced AI models like this can significantly enhance early detection and treatment planning, ultimately improving patient outcomes. A big thank you to everyone who supported and provided valuable feedback throughout this project. I look forward to exploring more innovative solutions. Stay tuned for more updates! #MachineLearning #DeepLearning #HealthcareAI #MedicalImaging #Innovation #ArtificialIntelligence #ChestXray #DataScience

#Technews John Hopfield and Geoffrey Hinton have been awarded the 2024 Nobel Prize in Physics for their pioneering work on artificial neural networks. Their discoveries laid the groundwork for modern AI, enabling breakthroughs in technologies like image and speech recognition, self-driving cars, and medical diagnostics. This foundational work continues to shape how AI drives innovation today and raises important questions about the ethical use of machine learning. As AI continues to evolve, the focus remains on humans using it ethically and responsibly for the greater good. GPIT Academy offers courses in AI/Machine learning, reach out now to register (15th-28th Oct, 2024) and start your tech journey. #GPIT #weprovidetechsolutions #NobelPrize2024 #AI #MachineLearning #NeuralNetworks #Innovation #TechNews #ResponsibleAI

We hope you’re ready for the third MUDS workshop! This week, we’re diving into the fascinating world of 𝗖𝗼𝗻𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗹 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗖𝗡𝗡𝘀) and exploring how they revolutionised Medical Image Analysis. 𝗘𝘃𝗲𝗻𝘁 𝗗𝗲𝘁𝗮𝗶𝗹𝘀: 📅 𝗗𝗮𝘁𝗲: Wednesday, 27th of November 🕒 𝗧𝗶𝗺𝗲: 15:00 - 16:30 📍 𝗩𝗲𝗻𝘂𝗲: Simon_TH D 𝗪𝗵𝗮𝘁 𝗬𝗼𝘂'𝗹𝗹 𝗟𝗲𝗮𝗿𝗻: - Understand how Convolutional Neural Networks (CNNs) work. - Build a Computer Vision pipeline using PyTorch. - Train a CNN to classify organs from CT scans. Computer Vision tools are transforming the field of radiology, enabling faster, more accurate diagnoses. Whether you're passionate about healthcare, AI, or both, this workshop is the perfect opportunity to take your skills to the next level. 𝗟𝗲𝘃𝗲𝗹: 𝗜𝗻𝘁𝗲𝗿𝗺𝗲𝗱𝗶𝗮𝘁𝗲. To get the most out of this session, we recommend reviewing the material from our "Intro to Deep Learning" workshop available at bit.ly/MUDS-IntroDL Don’t miss this chance to enhance your knowledge and network with fellow enthusiasts! Register here https://lnkd.in/eGAfpna9 For questions, contact us on Instagram: @uomdss See you on Wednesday!

The future of AI is merging with biology, and Neuro is leading the way by using organoid brains—miniature, lab-grown brain models—to perform complex AI computations. This groundbreaking platform taps into the power of biological neural networks for tasks previously limited to traditional AI systems. Here’s how Neuro’s tech is transforming the AI landscape: - Organoid Intelligence (OI): Neuro uses brain organoids to simulate natural brain activity, combining the adaptability and efficiency of biological neural networks with the precision of machine learning. - Hybrid AI: By integrating bio-based computing with machine learning algorithms, Neuro creates a hybrid AI that is capable of learning and adapting more efficiently than silicon-based models. - Energy Efficiency: Organoid brains mimic biological processes, consuming far less energy than traditional AI systems while performing complex computations, making Neuro a green AI solution. - Advanced Problem-Solving: With biological networks at the core, Neuro excels at handling tasks like pattern recognition, decision-making, and problem-solving, leveraging the innate learning capacity of brain cells. - Cutting-Edge Research: Neuro is at the intersection of neuroscience and AI, advancing our understanding of how biological systems can revolutionize computation, potentially leading to smarter and more intuitive AI systems. This is not just AI—this is the future of organoid intelligence, where biology meets technology. #DidYouKnow #OrganoidBrains #AI #NeuroPlatform #OrganoidIntelligence #BioAI #TechInnovation #ArtificialIntelligence

: 🩻 Thrilled to announce my participation in the RSNA AI Challenge for detecting degenerative spine conditions using MRI images! 🩻 This competition has been an incredible journey, pushing my skills in deep learning and medical imaging to new heights. 🌟 During the competition, I: -Deep Learning in Medical Imaging: Understanding how AI can assist radiologists in diagnosing complex conditions like spondylosis. In particular, using Convolutional Neural Networks (CNNs) proved effective for image classification tasks. -Explored image annotation methods, understanding their crucial role in training robust models and improving diagnostic accuracy. -Utilized diverse datasets from multiple institutions, enhancing my ability to create solutions that cater to real-world medical scenarios. -Gained insights into model evaluation, focusing on weighted log-loss metrics to assess performance across different severity levels of spine conditions. 💡 This challenge has reinforced my passion for leveraging AI and machine learning in healthcare. The potential to aid in accurate diagnosis and improve patient outcomes is truly inspiring! This experience has been invaluable, and I look forward to applying these new skills to future projects in the intersection of technology and healthcare. 🚀 #Kaggle #RSNAChallenge #AIinHealthcare #MedicalImaging #DeepLearning #MachineLearning #SpineHealth #DataScience #ContinuousLearning #ArtificialIntelligence

Our following works are accepted for publication: 1. "Softflatten-net: A deep convolutional neural network design for Monkeypox Classification from Digital Skin Lesions Images"; IEEE Sensors Journal. 2. "Classification of Lung Disorders in Chest Multi-modal images using Hyper-Parameter Tuning and Modified Resnet50"; Multimedia Tools and Applications. 3. "Glaucoma Detection with Explainable AI using Convolutional Neural Networks based Feature Extraction and Machine Learning Classifiers"; IET Image Processing.

At the #ScaDSAISummerSchool2024 we are pleased to welcome Prof. Dr. Oliver Koch, Heisenberg Professor at the University of Münster. Koch will present to our audience on the transformative impact of AI in drug discovery. His presentation will explore how AI is revolutionizing drug discovery to achieve previously unattainable breakthroughs. He will cover the integration of computational methods and machine learning, highlighting advances and the importance of the underlying data. #AI #BigData #DrugDiscovery #Innovation #PharmaceuticalResearch #ScaDSAI #Leipzig #FutureOfMedicine

📺Tune in tonight at 9 p.m. ET / 8 p.m. CT to watch a special on "AI Revolution" by GBH about the advances in science from AI. Specifically, AI has paved the way for advances in prosthetic patient care. "The film follows Miles — whose left arm was amputated after an accident a decade ago — as he visits a bioengineering company called Coapt | Myo Pattern Recognition. The company has developed a machine learning algorithm that can interpret faint electromyographic (EMG) signals from amputees to allow them more control of myoelectric limbs. CEO Blair Lock attached Miles to a virtual prosthetic depicted on a screen, in order to begin the process of training the AI model which will be in his new arm." https://lnkd.in/ew63kbfP #LLPR #llpregistry #limbloss #limbdifference #orthotics #prosthetics #orthoticsandprosthetics #limbpreservation #weareoandp #limbdifferenceawareness #limblossawareness #activityishealth #HealthcareInnovation #HealthTech #DigitalHealth #InnovationInHealth #FutureOfHealth #TechInHealthcare #MedicalInnovation #HealthcareProfessionals #OandP

🌟 Understanding McCulloch-Pitts Neurons 🌟 Did you know that the foundation of modern Artificial Neural Networks stems from the pioneering work of McCulloch and Pitts in 1943? 🤔 Their neuron model was a breakthrough in computational neuroscience, introducing the idea of simulating brain-like decision-making using binary logic. Here’s a quick dive into how it works: 1️⃣ Inputs and Thresholds: The neuron takes excitatory inputs (X1, X2) and optionally inhibitory inputs (I) to determine whether to "fire" or not. 2️⃣ Summation and Comparison: The model sums the excitatory inputs and compares the sum against a predefined threshold value (θ). 3️⃣ Binary Output: If the sum is greater than or equal to θ, the neuron fires (output = 1). Otherwise, it doesn’t fire (output = 0). 💡 In Example 1, with X1 = On, X2 = On, and θ = 1, the neuron fires because the sum of inputs (2) exceeds the threshold. 💡 In Example 2, with X1 = On, X2 = Off, and θ = 2, the neuron doesn’t fire because the sum (1) is less than the threshold. Why is this important? The McCulloch-Pitts neuron laid the groundwork for perceptrons and deep learning models, enabling complex decision-making and pattern recognition in today’s AI systems. 🔗 Let’s connect to explore how these foundational concepts translate into modern AI solutions! #ArtificialIntelligence #DeepLearning #NeuralNetworks #McCullochPittsNeuron #AIInnovation #Innodatatics