Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification

Understand

In recent years, the availability of digitized Whole Slide Images (WSIs) has enabled the use of deep learning-based computer vision techniques for automated disease diagnosis.

• However, WSIs present unique computational and algorithmic challenges.
• WSIs are gigapixel-sized ($\sim$100K pixels), making them infeasible to be used directly for training deep neural networks.
• Also, often only slide-level labels are available for training as detailed annotations are tedious and can be time-consuming for experts.