Image Recognition Innovations In AI Research

𝗡𝗲𝘄 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱! Medical imaging is packed with hidden clinical biomarkers, but privacy hurdles and data scarcity often keep this treasure trove locked away from AI innovation. Frustrating, right? That’s exactly what inspired me and Abdullah Hosseini to ask: Can we generate synthetic medical images that not only look real, but also preserve the critical biomarkers clinicians rely on? So, we dove in. Using cutting-edge diffusion models fused with Swin-transformer networks, we generated synthetic images across three modalities—radiology (chest X-rays), ophthalmology (OCT), and histopathology (breast cancer slides). The big question: 𝗗𝗼 𝘁𝗵𝗲𝘀𝗲 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗶𝗺𝗮𝗴𝗲𝘀 𝗸𝗲𝗲𝗽 𝘁𝗵𝗲 𝘀𝘂𝗯𝘁𝗹𝗲, 𝗱𝗶𝘀𝗲𝗮𝘀𝗲-𝗱𝗲𝗳𝗶𝗻𝗶𝗻𝗴 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗶𝗻𝘁𝗮𝗰𝘁? • Our diffusion models faithfully preserved key biomarkers—like lung markings in X-rays and retinal abnormalities in OCT—across all datasets. • Classifiers trained only on synthetic data performed nearly as well as those trained on real images, with F1 and AUC scores hitting 0.8–0.99. • No statistically significant difference in diagnostic performance—meaning synthetic data could stand in for real data in many AI tasks, while protecting patient privacy. This work shows synthetic data isn’t just a lookalike—it’s a powerful, privacy-preserving tool for research, clinical AI, and education. Imagine sharing and scaling medical data without the headaches of privacy risk or limited access! Read the full paper: https://lnkd.in/eW6TM9H2 Get the code & datasets: https://lnkd.in/ek4wSkg3 #AI #Innovation #SyntheticData #DiffusionModels #MedicalImaging #HealthcareInnovation #DigitalHealth #Frontiers #WeillCornell #HealthTech #HealthcareAI #PrivacyPreservingAI #GenerativeAI #Biomarkers #MachineLearning #Qatar #MENA #MiddleEast #NorthAfrica #MENAIRegion #MENAInnovation #UAE #UnitedArabEmirates #SaudiArabia #KSA #Egypt AI Innovation Lab Weill Cornell Medicine Weill Cornell Medicine - Qatar Cornell Tech Cornell University

Imagine detecting hidden defects inside batteries, composites, polymers, and other critical components without cutting them open. New research shows how AI can "see through" materials using infrared thermography and it works with incredibly limited data. Traditional methods like X-rays can be expensive, slow, and sometimes hazardous. Meanwhile, supervised AI needs thousands of labeled defect samples that manufacturers rarely have. The Innovation: Researchers developed DAE-SWnet, an unsupervised AI system that: * Generates 16,000 training images from just 2 defective samples using pulsed thermography * Uses deep autoencoders to filter noise from thermal data * Employs Swin Transformers for precise defect segmentation without human labeling

The saying "more data beats clever algorithms" is not always so. In new research from Amazon, we show that using AI can turn this apparent truism on its head. Anomaly detection and localization is a crucial technology in identifying and pinpointing irregularities within datasets or images, serving as a cornerstone for ensuring quality and safety in various sectors, including manufacturing and healthcare. Finding them quickly, reliably, at scale matters, so automation is key. The challenge is that anomalies - by definition! - are usually rare and hard to detect - making it hard to gather enough data to train a model to find them automatically. Using AI, Amazon has developed a new method to significantly enhance anomaly detection and localization in images, which not only addresses the challenges of data scarcity and diversity but also sets a new benchmark in utilizing generative AI for augmenting datasets. Here's how it works... 1️⃣ Data Collection: The process starts by gathering existing images of products to serve as a base for learning. 2️⃣ Image Generation: Using diffusion models, the AI creates new images that include potential defects or variations not present in the original dataset. 3️⃣ Training: The AI is trained on both the original and generated images, learning to identify what constitutes a "normal" versus an anomalous one. 4️⃣ Anomaly Detection: Once trained, the AI can analyze new images, detecting and localizing anomalies with enhanced accuracy, thanks to the diverse examples it learned from. The results are encouraging, and show that 'big' quantities of data can be less important than high quality, diverse data when building autonomous systems. Nice work from the Amazon science team. The full paper is linked below. #genai #ai #amazon