Hear from Professor Lena Maier-Hein, board member of the MICCAI Society, as she outlines highlights from the MICCAI 2020 challenges, their statistics, and recent developments in biomedical image analysis.
Deep learning is an essential medical imaging tool for helping clinicians to quickly read images, calculate measurements, monitor changes, and identify urgent needs. Explore the latest research presented during the Medical Image Computing and Computer-Assisted Intervention (MICCAI) conference at NVIDIA's GPU Technology Conference (GTC).
Hear from Professor Lena Maier-Hein, board member of the MICCAI Society, as she outlines highlights from the MICCAI 2020 challenges, their statistics, and recent developments in biomedical image analysis.
LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation
Weakly Supervised One-Stage Vision and Language-Referred Object Detection Using Large-Scale Pneumonia and Pneumothorax Datasets
GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-Tuning for Alzheimer’s Disease Diagnosis from MRI
Automated Pancreas Segmentation Using Multi-Institutional Collaborative Deep Learning
Federated Learning for Breast Density Classification: A Real-World Implementation
Federated Simulation for Medical Imaging
| Monday, 10/ 5 | Tuesday, 10/6 | Wednesday, 10/7 | Thursday, OCT 10/8 |
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| Keynote by NVIDIA CEO Jensen Huang > 06:00 PDT (UTC - 07:00) |
Braindate: Roughing out the architecture for next-generation solutions in hospitals and healthcare > 09:00 - 09:45 PDT (UTC-07:00) |
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| Architecting AI-Powered Medical Instruments > 11:00 - 11:50 PDT (UTC-07:00) |
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| Braindate: What's the most effective way to integrate apps into a medical imaging ecosystem > 14:00 - 14:45 PDT (UTC-07:00) |
Braindate: What are some of the ways to integrate apps into the hospital ecosystem (like EMRs) > 13:00 - 13:45 PDT (UTC-07:00) |
What's the best ways to address virtual desktop infrastructure VDI in hospitals and healthcare? > 14:00 - 14:45 PDT (UTC-07:00) |
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| Dinner with Strangers: How to put AI trained models in action in a medical imaging environment > 17:00 - 18:00 PDT (UTC-07:00) |
Dinner with Strangers: Clara Imaging for training applications > 17:00 - 18:00 PDT (UTC-07:00) |
MONAI (Medical Open Network for AI) is a freely available, community-supported, open-source PyTorch-based framework for deep learning in medical imaging. It provides domain-optimized foundational capabilities for developing medical imaging training workflows in a native PyTorch paradigm. On September 30 - October 2, in collaboration with the MICCAI Educational Initiative, we brought the first-ever MONAI Bootcamp to medical imaging researchers, with training modules, an architectural deep dive, and an open challenge.
Check out this year’s winners from the MONAI Bootcamp, which involved applying MONAI tools in a COVID-19 classification challenge