Image Recognition for Healthcare Diagnostics with MONAI

What to expect?

Advancements in computer vision capabilities are impacting the healthcare and life sciences industry. Vision based activities, such as healthcare diagnostics, can be greatly aided with the help of AI and improve the quality of care for patients and improve the productivity of healthcare professionals. In this hands-on lab, you will learn how to use MONAI to train an image segmentation model identifying spleens and how to leverage Weights & Biases to keep track of various experiments and performance metrics. We will walk you through setting up the environment, explain different code blocks and tools as we execute the Jupyter Notebook, and then deploy the model to test its performance on specific blocks of text. MONAI is an open-source set of frameworks built for accelerating research and clinical collaboration in Medical Imaging, and was co-created by NVIDIA and King’s College London. Weights and Biases enables storing all ML experiments and research in one place, including model weights and datasets, which are handy when comparing experiments.