Enhanced StarGAN: AI-Powered Grading of Inhalation Injuries via Bronchoscopy скачать в хорошем качестве

Enhanced StarGAN: AI-Powered Grading of Inhalation Injuries via Bronchoscopy

🫁 Can AI objectively grade inhalation injuries from bronchoscopy images? In this episode, we explore a cutting-edge deep learning framework developed by researchers at Texas Tech University, combining Enhanced StarGAN and Swin Transformer for more reliable and realistic injury classification in burn patients. ━━━━━━━━━━━━━━━━━ 🔍 Key Highlights: • ⚙️ Enhanced StarGAN: A novel generative model integrating Patch Loss and SSIM Loss • 📈 Data Efficiency: Overcame extreme data scarcity using GAN-based augmentation • 🧠 Ventilation-Based Grading: Replaces subjective AIS score with ventilation duration • 🎯 Performance Boost: Classification accuracy jumped from 66.67% ➝ 77.78% ━━━━━━━━━━━━━━━━━ 📉 Image Quality Matters • Enhanced StarGAN achieved an industry-leading FID score of 30.06 • Burn surgeons verified synthetic image realism and clinical relevance • Improved detection in underrepresented grades (like Grade 6: 100% accuracy) ━━━━━━━━━━━━━━━━━ 💡 Real-World Impact: • Assists clinicians with more objective and consistent diagnosis • Addresses class imbalance, small datasets, and annotation barriers • Demonstrates AI’s potential to improve burn care and critical care outcomes ━━━━━━━━━━━━━━━━━ 🧪 Built With: • Bronchoscopy images from 22 ventilated burn patients • Swin Transformer + Transfer Learning • CycleGAN, StarGAN, CUT vs Enhanced StarGAN benchmarking ━━━━━━━━━━━━━━━━━ 🔗 Paper Abstract: https://arxiv.org/abs/2505.08517 📺 More episodes:    / @openlifesciai   🎧 Listen on Spotify: https://open.spotify.com/show/4edRuST... #MedicalAI #GANs #Bronchoscopy #HealthTech #InhalationInjury #AIResearch