Advancing computational pathology with deep learning: from patches to gigapixel image-level classification

D. Tellez

  • Promotor: J. van der Laak and N. Karssemeijer
  • Copromotor: F. Ciompi and G. Litjens
  • Graduation year: 2021
  • Radboud University, Nijmegen


The main focus of this work is to investigate novel deep learning based methodologies to improve breast cancer prognostic tools within the context of Computational Pathology. This research can be divided into three key blocks:

  1. Fundamental challenges in Computational Pathology. We address some of the issues that arise when developing deep learning based models across applications and organs. First, scaling the generation of pixel-level annotated data (Chapter 2). Second, addressing intra- and inter-center stain variation (Chapters 2 and 3). Third, developing accurate and fast models to process entire whole-slide images (Chapters 2 and 4).

  2. Automating the core component of breast cancer grading: performing mitosis detection at scale, that is, processing thousands of unseen multicenter entire whole-slide images, while deriving actionable insights for pathologists (Chapter 2).

  3. Performing whole-slide image classification. We propose a method that enables feeding entire whole-slide images to a single deep learning based model , targeting patient-level labels and outcome data such as overall survival(Chapters 4 and 5).