Thesis:

Histopathological diagnosis of breast cancer using machine learning, Ehteshami Bejnordi, B., 2017 LQ HQ Chromatin pattern analysis of cell nuclei for improved cervical cancer screening, Ehteshami Bejnordi, B., 2013

Journals:

Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies.. Ehteshami Bejnordi, B., Mullooly, M., Pfeiffer, R.M., Fan, S., Vacek, P.M. Weaver, D.L., Herschorn, S., Brinton, L.A. van Ginneken, B., Karssemeijer, N., Beck, A.H., Gierach, G.L., van der Laak, J., Sherman, M.E., 2018;31(10):1502-1512. Modern Pathology.
Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. Ehteshami Bejnordi, B., Veta, M., van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J., 2017;318(22):2199-2210. doi:10.1001/jama.2017.14585.

Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images Ehteshami Bejnordi, B., Zuidhof, G., Balkenhol, M., Hermsen, M., Bult, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J., Journal of Medical Imaging 2017; 4(4), 044504, doi: 10.1117/1.JMI.4.4.044504.

A survey on deep learning in medical image analysis Litjens, G., Kooi, T., Ehteshami Bejnordi, B., Setio, A.A.A., Ciompi, F., Ghafoorian, M., van der Laak, J.A., van Ginneken, B. and Sanchez, C.I., Medical Image Analysis 2017;42:60-88.

3D volume reconstruction from serial breast specimen radiographs for mapping between histology and 3D whole specimen imaging. Mertzanidou, T., Hipwell, J.H., Reis, S., Hawkes, D.J., Ehteshami Bejnordi, B., Dalmis, M., Vreemann, S., Platel, B., der Laak, J., Karssemeijer, N. and Hermsen, M., Bult, P., Mann, R. Medical physics 2017, 44 (3).

Automated detection of DCIS in whole-slide H&E stained breast histopathology images Ehteshami Bejnordi, B., Balkenhol, M., Litjens, G., Holland, R., Bult, P., Karssemeijer, N. and van der Laak, J., IEEE transactions on medical imaging 2016, 35(9), pp.2141-2150.

Stain specific standardization of whole-slide histopathological images Ehteshami Bejnordi, B., Litjens, G., Timofeeva, N., Otte-Holler, I., Homeyer, A., Karssemeijer, N., and van der Laak, J., IEEE transactions on medical imaging 2016, 35(2), 404-415. Source code | Windows | Docker


International Conferences:

Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images Ehteshami Bejnordi, B., Linz, J., Glass, B., Mullooly, M., Gierach, G.L., Sherman, M.E., Karssemeijer, N., van der Laak, J. and Beck, A.H., in: IEEE International Symposium on Biomedical Imaging, 2017, pages 929-932.

The importance of stain normalization in colorectal tissue classification with convolutional networks, Ciompi, F., Geessink, O., Ehteshami Bejnordi, B., de Souza, G.S., Baidoshvili, A., Litjens, G., van Ginneken, B., Nagtegaal, I. and van der Laak, J., IEEE International Symposium on Biomedical Imaging, 2017, pages 160-163.

A multi-scale superpixel classification approach to the detection of regions of interest in whole slide histopathology images Ehteshami Bejnordi, B., Litjens, G., Hermsen, M., Karssemeijer, N. and van der Laak, J., in SPIE Medical Imaging, pp. 94200H-94200H, 2015.

Automated detection of prostate cancer in digitized whole-slide images of H and E-stained biopsy specimens, Litjens, G., Ehteshami Bejnordi, B., Timofeeva, N., Swadi, G., Kovacs, I., Hulsbergen-van de Kaa, C. and van der Laak, J., in SPIE Medical Imaging, pp. 94200H-94200H, 2015.

A novel spherical shell filter for reducing false positives in automatic detection of pulmonary nodules in thoracic CT scans, van de Leemput, S., Dorssers, F. and Ehteshami Bejnordi, B., in SPIE Medical Imaging, pp. 94200H-94200H, 2015.

Quantitative analysis of stain variability in histology slides and an algorithm for standardization, Ehteshami Bejnordi, B., Timofeeva, N., Otte-Höller, I., Karssemeijer, N. and van der Laak, J., in SPIE Medical Imaging, pp. 904108-904108, 2014.

A fully automatic unsupervised segmentation framework for the brain tissues in MR images, Mahmood, Q., Chodorowski, A., Ehteshami Bejnordi, B. and Persson, M., in SPIE Medical Imaging, pp. 904108-904108, 2014.

Novel chromatin texture features for the classification of pap smears, Ehteshami Bejnordi, B. Moshavegh, R., Sujathan, K., Malm, P., Bengtsson, E. and Mehnert, A., in SPIE Medical Imaging (pp. 867608-867608), 2013.

Automated segmentation of free-lying cell nuclei in Pap smears for malignancy-associated change analysis, Moshavegh, R., Ehteshami Bejnordi, B., Mehnert, A., Sujathan, K., Malm, P. and Bengtsson, E., in Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, pp. 5372-5375. IEEE, 2012.