Histopathological diagnosis of breast cancer using machine learning

B. Bejnordi

Promotor: N. Karssemeijer
Copromotor: J. A. W. M. van der Laak and G. Litjens
Radboud University, Nijmegen, The Netherlands
2017

Abstract

The primary aim of the 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. The secondary aim of the thesis was stated as:to identify new diagnostic bio-markers for the detection of invasive breast cancer in H&E stained breast histopathological images.

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