Explore the best of MICCAI’s groundbreaking research in deep learning and medical imaging at GTC.

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).

An Inside Look at MICCAI 2020

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.


Featured MICCAI Workshops and Research

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


MONAI Bootcamp for Medical Imaging

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.

MONAI Challenge Winners

Check out this year’s winners from the MONAI Bootcamp, which involved applying MONAI tools in a COVID-19 classification challenge


Learn more about NVIDIA in medical imaging.