Robustness analysis of AI models designed for histopathology tasks








About project

Deep Learning for Pathology is known to be susceptible to robustness issues. Robustness refers to how reliably and accurately deep learning models behave or perform across different types of data; e.g. different scanner vendors, hospitals, or patient groups. In this study we aim to:

  1. Define proper metrics with which robustness can be measured.
  2. Consider scenarios which may affect model performance such as:
  • Sites (labs)
  • Staining procedure
  • Scanner and sensor type
  • Rotations
  1. Improve model robustness for WSI classification and segmentation tasks with a novel method.For this project, I collaborate with


Our publications & software