We’re excited that our team members Mark Romme, Jade Yu Tseng, and Dang Nguyen will be representing Kaiko at Ray Summit in San Francisco, happening September 30 – October 2. At the summit, Mark will discuss how Kaiko leverages Ray in our model development cycle to iterate multimodal oncology foundation models in the clinic. His talk will explore how we combine pre-trained large language models (LLMs) with Vision Transformers (ViTs) to empower pathologists to make faster, more accurate diagnoses. He’ll also dive into our process for deploying and serving the model in clinical research settings, focusing on its real-time data processing and rigorous validation capabilities. Attending Ray Summit? We'd love to connect! Feel free to reach out or stop by our session to learn more about how we’re advancing oncology care with AI.
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18 Breast Cancer Classification.pdf Explore my interactive demos showcasing key concepts in Statistics, Data Science, Machine Learning, and Deep Learning. Each demo is designed to simplify complex topics and make learning engaging and intuitive. Link to my Interactive Demo here:https://lnkd.in/dbzu7-qm
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Red Colour highlighted chapters already allocated, others you can pick and write to us... dates 22/09/2024, for an outline along with a brief CV ******************** 14 Challenges in Integration of computational approaches with Clinical Practice 16 Statement On the Effectiveness of AI And ML In Cancer Care There are only a few of these chapters remaining; please pick one.
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enabling digital services for Student Loan related activities while maintaining the highest security standard, the most compliant personal data protection and customer-centric data-driven innovation.
📢 New blog post alert! Check out our latest case study on utilizing large language models (LLMs) in the field of medical rule validation. The post explores the innovative LLMeDiff approach and its application in testing a core component of the Cancer Registry of Norway's cancer registration support system. Get exclusive insights into our experimentation with LLMs, medical rule engine implementations, and real medical rules. Click here to read the full post: https://bit.ly/3vKRePj #socialmediamarketing #casestudy #medicaltechnology
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Tired of M.Tech Thesis Challenges? 😩 Discover expert solutions at Techsparks and conquer your thesis with ease! 🎓💡 💻 Explore Artificial Intelligence (AI) Topics: 🌿 Plant Disease Detection 🏃♂️ Human Activity Detection 🫁 Lung Cancer Detection 🌐 Network Traffic Classification 🎙️ Speech Emotion Recognition 📘 Get Free Topics at: techsparks.co.in Unlock your thesis potential with Techsparks’ comprehensive support! 🚀✨ . . #ThesisHelp #Techsparks #ArtificialIntelligence #AIResearch #MTechThesis #PhDThesis #ResearchSupport #AcademicWriting
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Machine Learning Engineer || LLM || Opensource Contributor || Ex-Student Coordinator @SAP-CodeUnnati || Ex-Intern @HexaCoder Tech.
𝗧𝗵𝗿𝗶𝗹𝗹𝗲𝗱 𝘁𝗼 𝗮𝗻𝗻𝗼𝘂𝗻𝗰𝗲 𝘁𝗵𝗲 𝗽𝘂𝗯𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗼𝘂𝗿 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗽𝗮𝗽𝗲𝗿, "𝗦𝘂𝗿𝘃𝗲𝘆 𝗮𝗻𝗱 𝗖𝘂𝗿𝗿𝗲𝗻𝘁 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗳𝗼𝗿 𝗕𝗿𝗲𝗮𝘀𝘁 𝗖𝗮𝗻𝗰𝗲𝗿 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻"! 📝🔬 Early detection of breast cancer is critical for improving patient outcomes. In this paper, we explore various machine learning approaches, including traditional machine learning, deep learning with optimization and preprocessing, and deep learning with transfer learning, to assess their efficacy in breast cancer detection. We delve into the advantages and disadvantages of each method, providing valuable insights for researchers in this field. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗵𝗮𝘀 𝘁𝗵𝗲 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝘁𝗼 𝗿𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗲 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗯𝘆 𝗮𝘀𝘀𝗶𝘀𝘁𝗶𝗻𝗴 𝗽𝗵𝘆𝘀𝗶𝗰𝗶𝗮𝗻𝘀 𝗮𝗻𝗱 𝗶𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴 𝗽𝗮𝘁𝗶𝗲𝗻𝘁 𝗰𝗮𝗿𝗲. This research contributes to the growing body of knowledge on utilizing machine learning for breast cancer detection, a crucial area due to the shortage of radiologists and the need for faster, more accurate diagnoses. I'd like to express my gratitude to my mentor, Dr.Nikita Bhatt, and my co-authors, Yagnik Poshiya, Neel Patel, and Nirmi Patel for their invaluable contributions to this research. I'd love to hear your thoughts and connect with other researchers interested in applying machine learning to healthcare challenges! Feel free to share this post to raise awareness about breast cancer and the potential of machine learning in early detection! 𝘼𝙨 𝙖𝙡𝙬𝙖𝙮𝙨, 𝙆𝙚𝙚𝙥 𝙇𝙚𝙖𝙧𝙣𝙞𝙣𝙜, 𝙆𝙚𝙚𝙥 𝙂𝙧𝙤𝙬𝙞𝙣𝙜! 😎 🚀 #breastcancer #machinelearning #healthcare #research #earlydetection #deeplearning #researchpaper
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🌟🚀 Hungarian physicians are pioneering the future of breast cancer detection with groundbreaking AI technology! 💡 A new deep-learning model can now spot breast cancer up to 4 years before it actually develops. Using data from over 90,000 mammograms and advanced pattern recognition, this AI helps identify potential cancerous spots years ahead of time. 🩺📈 Developed by MIT’s Computer Science and Artificial Intelligence Laboratory, this model examines subtle changes in breast tissue that even the sharpest eyes might miss. The result? Earlier detection and more effective prevention strategies! 🏥🔬 #thedoctorpreneuracademy #BreastCancerAwareness #AITechnology #EarlyDetection #HealthcareInnovation #MedicalBreakthroughs #cancerresearchuk
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Microsoft has developed a generative AI-based medical image analysis model called BiomedParse that can improve early diagnosis of life-threatening diseases such as cancer. BiomedParse combines object recognition, detection, and segmentation to get faster and more precise clinical insights. By combining these tasks, the model can more effectively identify, locate, and map tumor boundaries on complex medical images. Radiologists can use simple natural language prompts to direct the model’s attention to specific areas of interest. #AI #Microsoft #cancer https://lnkd.in/gJagS-DK
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RSNA 2024 is a special milestone for us— our first meeting where Volpara and Lunit Cancer Screening are one company. By combining our expertise in breast and chest imaging, supported by 130M+ images and 400+ peer-reviewed studies, we’re focused on delivering practical AI solutions that truly support radiologists and improve patient outcomes. We’re excited about what’s ahead—innovating in areas like image-based risk, predictive analytics, and smarter workflows. But it’s not just about the tech; it’s about working with you to make a difference. 🗓️ Stop by our booths (South Hall AI showcase Volpara #4705 | Lunit #4929) to explore our mammography AI ecosystem and chat with us about how we can support your practice.
🌟 #Synergy in Action : Day 1 at RSNA with Lunit x Volpara 🌟 Our first day at RSNA has been nothing short of incredible! Lunit and Volpara Health together are demonstrating the power of our AI solutions in cancer detection and patient care, driven by a shared passion to conquer cancer with AI and save more lives. By combining Lunit and Volpara’s expertise with access to a massive database of images, we’re building a powerful #ecosystem that enhances accuracy, speed, and quality at every step of cancer screening and care. 🌱 Stop by our booths in the next couple of days to: ➜ Learn how AI can elevate your clinical workflow. ➜ Take the INSIGHT Challenge and see how AI compares to your expertise. ➜ Get hands-on with world-class AI solutions from Lunit and Volpara. 📍 Visit us at RSNA and discover our synergy for yourself : Lunit #4929, Volpara #4705 📆 Dec. 1st ~ Dec. 4th , 2024 📣 Secure your meeting with us today and explore how our AI solutions : https://bit.ly/4egecOs #lunit #volpara #ecosystem #rsna #rsna2024
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Research-Intern@IIIT(Allahabad) || Ex-Intern@C-DAC || Ex- Intern@MNNIT(Prayagraj) || Rajiv Gandhi Institute of Petroleum Technology (RGIPT)|| JEE ADV 23(fresher)|| Project@neural network
Pleasure to share my latest project, where I applied four powerful machine learning algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression, and Naive Bayes—to build a robust breast cancer classifier. Project Highlights: 1. Dataset: Leveraged the Breast Cancer Wisconsin Dataset, widely used for research, to classify tumors as malignant or benign. 2. Algorithms: Explored both simple and complex models—ranging from the probabilistic approach of Naive Bayes to the more sophisticated SVM—showing the versatility of machine learning in healthcare. 3. Evaluation: Assessed model performance using metrics like accuracy, precision, recall, and F1-score, aiming for reliable diagnostic outcomes. 4. Key Takeaways: Different algorithms excel in different scenarios, emphasizing the need for tailored solutions when deploying AI in critical fields like oncology. This project reinforced my belief in the transformative potential of AI for improving early cancer detection. IMAGE DATASET -: https://lnkd.in/gbgkSBue ROC SOCRE -: 82 F1_SCORE -: .97 ACCURACY -: 96% #MachineLearning #BreastCancerAwareness #AIinHealthcare #KNN #SVM #NaiveBayes #LogisticRegression #DataScience #CancerResearch #HealthcareAI
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Ease Your M.Tech Thesis Worries with Techsparks—Your Source for Practical Solutions and Success! We provide several topics on Artificial Intelligence (AI): Plant Disease Detection Human Activity Detection Lung Cancer Detection Network Traffic Classification Speech Emotion Recognition For free topics, visit techsparks.co.in. . . #ThesisHelp #ArtificialIntelligence #Techsparks #MTechThesis #ResearchSupport #AcademicSuccess
