Weakly-Supervised, Large-Scale Computational Pathology for Diagnosis and Prognosis - Max Lu
Explore a comprehensive framework for developing interpretable diagnostic and prognostic machine learning models using digitized histopathology slides in this 52-minute conference talk by Max Lu from Stanford University. Learn about a scalable method that doesn't require manual annotation of regions of interest and can be applied to tens of thousands of samples. Discover applications ranging from cancer subtyping and prognosis to predicting primary origins of metastatic tumors. Gain insights into weakly-supervised, large-scale computational pathology techniques, including multiple instance learning, attention pooling, and data efficiency. Examine benchmarks, attention scores, and interactive demos showcasing the framework's capabilities in cell phone microscopy, prognosis, and primary origin prediction of metastatic tumors.