Explore few-shot learning techniques for chest X-ray diagnosis in this Stanford University seminar presented by Dr. Angshuman Paul. Delve into two novel methods: a discriminative ensemble trained on clinical images and a model utilizing both scientific literature and unlabeled clinical chest X-rays. Compare these approaches to existing few-shot learning methods and understand their superior performance. Gain insights into the challenges of few-shot learning in radiology image analysis and learn about ensemble models, bootstrap sampling, subspace sampling, and meta-learning frameworks. Examine the experimental results, including F1 scores and utility of design, and consider future directions in this field. Engage with the speaker's expertise in machine learning, medical imaging, and computer vision during the interactive Q&A session following the presentation.