MITOS-RCNN: Mitotic Figure Detection in Breast Cancer Histopathology Images Using Region Based Convolutional Neural Networks
Studies estimate that there will be 266,120 new cases
of invasive breast cancer and 40,920 breast cancer induced deaths
in the year of 2018 alone. Despite the pervasiveness of this
affliction, the current process to obtain an accurate breast cancer
prognosis is tedious and time consuming. It usually requires a
trained pathologist to manually examine histopathological images and
identify the features that characterize various cancer severity levels.
We propose MITOS-RCNN: a region based convolutional neural
network (RCNN) geared for small object detection to accurately
grade one of the three factors that characterize tumor belligerence
described by the Nottingham Grading System: mitotic count. Other
computational approaches to mitotic figure counting and detection
do not demonstrate ample recall or precision to be clinically viable.
Our models outperformed all previous participants in the ICPR 2012
challenge, the AMIDA 2013 challenge and the MITOS-ATYPIA-14
challenge along with recently published works. Our model achieved
an F- measure score of 0.955, a 6.11% improvement in accuracy from
the most accurate of the previously proposed models.
[1] M. Ghoncheh, Z. Pournamdar, and H. Salehiniya, “Incidence and
mortality and epidemiology of breast cancer in the world,” vol. 17, pp.
43–46, 06 2016.
[2] C. W Elston and I. Ellis, “Pathological prognostic factors in breast
cancer. i. the value of histological grade in breast cancer: experience
from a large study with long-term follow-up. c. w. elston & i. o. ellis.
histopathology 1991; 19; 403-410. author commentary,” vol. 41, pp.
151–2, discussion 152, 10 2002.
[3] L. Roux and D. Racoceanu, “Mitos & atypia detection of mitosis and
evaluation of nuclear atypia score in breast cancer histological images,”
06 2014.
[4] S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time
object detection with region proposal networks,” in Advances in Neural
Information Processing Systems 28, C. Cortes, N. D. Lawrence,
D. D. Lee, M. Sugiyama, and R. Garnett, Eds. Curran Associates,
Inc., 2015, pp. 91–99. (Online). Available: http://papers.nips.cc/paper/
5638-faster-r-cnn-towards-real-time-object-detection-with-region-\
proposal-networks.pdf.
[5] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard,
W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten
zip code recognition,” Neural Computation, vol. 1, no. 4, pp. 541–551,
Dec 1989.
[6] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification
with deep convolutional neural networks,” in Proceedings of the 25th
International Conference on Neural Information Processing Systems
- Volume 1, ser. NIPS’12. USA: Curran Associates Inc., 2012,
pp. 1097–1105. (Online). Available: http://dl.acm.org/citation.cfm?id=
2999134.2999257.
[7] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature
hierarchies for accurate object detection and semantic segmentation,”
ArXiv e-prints, nov 2013.
[8] R. B. Girshick, “Fast R-CNN,” CoRR, vol. abs/1504.08083, 2015.
(Online). Available: http://arxiv.org/abs/1504.08083.
[9] L. Roux, D. Racoceanu, N. Lomenie, M. Kulikova, H. Irshad, J. Klossa,
F. Capron, C. Genestie, G. Le Naour, and M. N Gurcan, “Mitosis
detection in breast cancer histological images an icpr 2012 contest,”
vol. 4, p. 8, 05 2013.
[10] M. Veta, P. van Diest, S. Willems, H. Wang, A. Madabhushi,
A. Cruz-Roa, F. Gonzalez, A. Larsen, J. Vestergaard, A. Dahl, D. Cirean,
J. Schmidhuber, A. Giusti, L. Gambardella, F. Tek, T. Walter, C. Wang,
S. Kondo, B. Matuszewski, F. Precioso, V. Snell, J. Kittler, T. de
Campos, A. Khan, N. Rajpoot, E. Arkoumani, M. Lacle, M. Viergever,
and J. Pluim, “Assessment of algorithms for mitosis detection in breast
cancer histopathology images,” Medical Image Analysis, vol. 20, no. 1,
pp. 237–248, 2 2015.
[11] D. C. Cires¸an, A. Giusti, L. M. Gambardella, and J. Schmidhuber,
“Mitosis detection in breast cancer histology images with deep
neural networks,” in Medical Image Computing and Computer-Assisted
Intervention – MICCAI 2013, K. Mori, I. Sakuma, Y. Sato, C. Barillot,
and N. Navab, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg,
2013, pp. 411–418.
[12] H. Chen, Q. Dou, X. Wang, J. Qin, and P. Heng, “Mitosis detection
in breast cancer histology images via deep cascaded networks,”
2016. (Online). Available: https://www.aaai.org/ocs/index.php/AAAI/
AAAI16/paper/view/11788/11717. [13] S. Albarqouni, C. Baur, F. Achilles, V. Belagiannis, S. Demirci, and
N. Navab, “Aggnet: Deep learning from crowds for mitosis detection in
breast cancer histology images,” IEEE Transactions on Medical Imaging,
vol. 35, no. 5, pp. 1313–1321, May 2016.
[14] M. Saha, C. Chakraborty, and D. Racoceanu, “Efficient deep learning
model for mitosis detection using breast histopathology images,” vol. 64,
12 2017.
[15] M. Macenko, M. Niethammer, J. Marron, D. Borland, J. Woosley,
X. Guan, C. Schmitt, and N. Thomas, “A method for normalizing
histology slides for quantitative analysis,” in Proceedings - 2009 IEEE
International Symposium on Biomedical Imaging: From Nano to Macro,
ISBI 2009, 2009, pp. 1107–1110.
[16] K. Simonyan and A. Zisserman, “Very deep convolutional networks for
large-scale image recognition,” 09 2014.
[17] C. Eggert, S. Brehm, A. Winschel, D. Zecha, and R. Lienhart, “A
closer look: Small object detection in faster r-cnn,” in 2017 IEEE
International Conference on Multimedia and Expo (ICME), vol. 00,
July 2017, pp. 421–426. (Online). Available: doi.ieeecomputersociety.
org/10.1109/ICME.2017.8019550.
[18] M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin,
S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga,
S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan,
P. Warden, M. Wicke, Y. Yu, and X. Zheng, “Tensorflow: A system
for large-scale machine learning,” in 12th USENIX Symposium on
Operating Systems Design and Implementation (OSDI 16), 2016,
pp. 265–283. (Online). Available: https://www.usenix.org/system/files/
conference/osdi16/osdi16-abadi.pdf.
[1] M. Ghoncheh, Z. Pournamdar, and H. Salehiniya, “Incidence and
mortality and epidemiology of breast cancer in the world,” vol. 17, pp.
43–46, 06 2016.
[2] C. W Elston and I. Ellis, “Pathological prognostic factors in breast
cancer. i. the value of histological grade in breast cancer: experience
from a large study with long-term follow-up. c. w. elston & i. o. ellis.
histopathology 1991; 19; 403-410. author commentary,” vol. 41, pp.
151–2, discussion 152, 10 2002.
[3] L. Roux and D. Racoceanu, “Mitos & atypia detection of mitosis and
evaluation of nuclear atypia score in breast cancer histological images,”
06 2014.
[4] S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time
object detection with region proposal networks,” in Advances in Neural
Information Processing Systems 28, C. Cortes, N. D. Lawrence,
D. D. Lee, M. Sugiyama, and R. Garnett, Eds. Curran Associates,
Inc., 2015, pp. 91–99. (Online). Available: http://papers.nips.cc/paper/
5638-faster-r-cnn-towards-real-time-object-detection-with-region-\
proposal-networks.pdf.
[5] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard,
W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten
zip code recognition,” Neural Computation, vol. 1, no. 4, pp. 541–551,
Dec 1989.
[6] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification
with deep convolutional neural networks,” in Proceedings of the 25th
International Conference on Neural Information Processing Systems
- Volume 1, ser. NIPS’12. USA: Curran Associates Inc., 2012,
pp. 1097–1105. (Online). Available: http://dl.acm.org/citation.cfm?id=
2999134.2999257.
[7] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature
hierarchies for accurate object detection and semantic segmentation,”
ArXiv e-prints, nov 2013.
[8] R. B. Girshick, “Fast R-CNN,” CoRR, vol. abs/1504.08083, 2015.
(Online). Available: http://arxiv.org/abs/1504.08083.
[9] L. Roux, D. Racoceanu, N. Lomenie, M. Kulikova, H. Irshad, J. Klossa,
F. Capron, C. Genestie, G. Le Naour, and M. N Gurcan, “Mitosis
detection in breast cancer histological images an icpr 2012 contest,”
vol. 4, p. 8, 05 2013.
[10] M. Veta, P. van Diest, S. Willems, H. Wang, A. Madabhushi,
A. Cruz-Roa, F. Gonzalez, A. Larsen, J. Vestergaard, A. Dahl, D. Cirean,
J. Schmidhuber, A. Giusti, L. Gambardella, F. Tek, T. Walter, C. Wang,
S. Kondo, B. Matuszewski, F. Precioso, V. Snell, J. Kittler, T. de
Campos, A. Khan, N. Rajpoot, E. Arkoumani, M. Lacle, M. Viergever,
and J. Pluim, “Assessment of algorithms for mitosis detection in breast
cancer histopathology images,” Medical Image Analysis, vol. 20, no. 1,
pp. 237–248, 2 2015.
[11] D. C. Cires¸an, A. Giusti, L. M. Gambardella, and J. Schmidhuber,
“Mitosis detection in breast cancer histology images with deep
neural networks,” in Medical Image Computing and Computer-Assisted
Intervention – MICCAI 2013, K. Mori, I. Sakuma, Y. Sato, C. Barillot,
and N. Navab, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg,
2013, pp. 411–418.
[12] H. Chen, Q. Dou, X. Wang, J. Qin, and P. Heng, “Mitosis detection
in breast cancer histology images via deep cascaded networks,”
2016. (Online). Available: https://www.aaai.org/ocs/index.php/AAAI/
AAAI16/paper/view/11788/11717. [13] S. Albarqouni, C. Baur, F. Achilles, V. Belagiannis, S. Demirci, and
N. Navab, “Aggnet: Deep learning from crowds for mitosis detection in
breast cancer histology images,” IEEE Transactions on Medical Imaging,
vol. 35, no. 5, pp. 1313–1321, May 2016.
[14] M. Saha, C. Chakraborty, and D. Racoceanu, “Efficient deep learning
model for mitosis detection using breast histopathology images,” vol. 64,
12 2017.
[15] M. Macenko, M. Niethammer, J. Marron, D. Borland, J. Woosley,
X. Guan, C. Schmitt, and N. Thomas, “A method for normalizing
histology slides for quantitative analysis,” in Proceedings - 2009 IEEE
International Symposium on Biomedical Imaging: From Nano to Macro,
ISBI 2009, 2009, pp. 1107–1110.
[16] K. Simonyan and A. Zisserman, “Very deep convolutional networks for
large-scale image recognition,” 09 2014.
[17] C. Eggert, S. Brehm, A. Winschel, D. Zecha, and R. Lienhart, “A
closer look: Small object detection in faster r-cnn,” in 2017 IEEE
International Conference on Multimedia and Expo (ICME), vol. 00,
July 2017, pp. 421–426. (Online). Available: doi.ieeecomputersociety.
org/10.1109/ICME.2017.8019550.
[18] M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin,
S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga,
S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan,
P. Warden, M. Wicke, Y. Yu, and X. Zheng, “Tensorflow: A system
for large-scale machine learning,” in 12th USENIX Symposium on
Operating Systems Design and Implementation (OSDI 16), 2016,
pp. 265–283. (Online). Available: https://www.usenix.org/system/files/
conference/osdi16/osdi16-abadi.pdf.
@article{"International Journal of Medical, Medicine and Health Sciences:78112", author = "Siddhant Rao", title = "MITOS-RCNN: Mitotic Figure Detection in Breast Cancer Histopathology Images Using Region Based Convolutional Neural Networks", abstract = "Studies estimate that there will be 266,120 new cases
of invasive breast cancer and 40,920 breast cancer induced deaths
in the year of 2018 alone. Despite the pervasiveness of this
affliction, the current process to obtain an accurate breast cancer
prognosis is tedious and time consuming. It usually requires a
trained pathologist to manually examine histopathological images and
identify the features that characterize various cancer severity levels.
We propose MITOS-RCNN: a region based convolutional neural
network (RCNN) geared for small object detection to accurately
grade one of the three factors that characterize tumor belligerence
described by the Nottingham Grading System: mitotic count. Other
computational approaches to mitotic figure counting and detection
do not demonstrate ample recall or precision to be clinically viable.
Our models outperformed all previous participants in the ICPR 2012
challenge, the AMIDA 2013 challenge and the MITOS-ATYPIA-14
challenge along with recently published works. Our model achieved
an F- measure score of 0.955, a 6.11% improvement in accuracy from
the most accurate of the previously proposed models.", keywords = "Object detection, histopathology, breast cancer, mitotic
count, deep learning, computer vision.", volume = "12", number = "10", pages = "514-7", }
