Abstract

Breast cancer (BC) is the second most leading cause of cancer deaths in women and BC metastasis accounts for the majority of deaths. Early detection of breast cancer metastasis in sentinel lymph nodes is of high importance for prediction and management of breast cancer progression. In this paper, we propose a novel deep learning framework for automatic detection of micro- and macro- metastasis in multi-gigapixel whole-slide images (WSIs) of sentinel lymph nodes. One of our main contributions is to incorporate a Bayesian solution for the optimization of network’s hyperparameters on one of the largest histology dataset, which leads to 5% gain in overall patch-based accuracy. Furthermore, we present an ensemble of two multi-resolution deep learning networks, one captures the cell level information and the other incorporates the contextual information to make the final prediction. Finally, we propose a two-step thresholding method to post-process the output of ensemble network. We evaluate our proposed method on the CAMELYON16 dataset, where we outperformed “human experts” and achieved the second best performance compared to 32 other competing methods.

Citation information

Koohbanani N.A., Qaisar T., Shaban M., Gamper J., Rajpoot N. (2018) Significance of Hyperparameter Optimization for Metastasis Detection in Breast Histology Images. In: Stoyanov D. et al. (eds) Computational Pathology and Ophthalmic Medical Image Analysis. OMIA 2018, COMPAY 2018. Lecture Notes in Computer Science, vol 11039. Springer, Cham

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