Histopathological diagnosis of breast cancer using machine learning

B. Bejnordi

  • Promotor: N. Karssemeijer
  • Copromotor: J. van der Laak and G. Litjens
  • Graduation year: 2017
  • Radboud University, Nijmegen


Application of machine learning to WSI is a promising yet largely unexplored field of research. The primary aim of the research described in this thesis was to develop automated systems for analysis of H&E stained breast histopathological images. This involved automatic detection of ductal carcinoma in-situ (DCIS), invasive, and metastatic breast cancer in whole-slide histopathological images. A secondary aim was to identify new diagnostic biomarkers for the detection of invasive breast cancer. To this end the research was undertaken with the following objectives:

  1. Development of an algorithm for standardization of H&E stained WSIs;

  2. Detection, classification and segmentation of primary breast cancer;

  3. Evaluation of the state of the art of machine learning algorithms for automatic detection of lymph nodes metastases;

  4. Identifying and leveraging new stromal biomarkers to improve breast cancer diagnostics.