A mammography classification model trained from image labels only





deep learning, neural networks, breast cancer screening, weakly supervised localization, high-resolution image classification


The Cancer Registry of Norway organises a population-based breast cancer screening program, where 250 000 women participate each year. The interpretation of the screening mammograms is a manual process, but deep neural networks are showing  potential in mammographic screening. Most methods focus on methods trained from pixel-level annotations, but these require expertise and are time-consuming to produce. Through the screenings, image level annotations are however readily available. In this work we present a few models trained from image level annotations from the Norwegian dataset: a holistic model, an attention model and an ensemble model. We compared their performance with that of pretrained models based on pixel-level annotations, trained on international datasets. From this we found that models trained on our local data with image-level annotation gave considerably better performance than the pretrained models from external data, although based on pixel-level annotations.


L. Abdelrahman et al. Convolutional neural networks for breast cancer detection in mammmography: A survey. Comput. Biol. Med. 113, 2021.

G. Carneiro et al. Unregistered multiview mammogram analysis with pre-trained deep learning models. In Int. Conf. on Medical Image Computing and Computer-Assisted Interv, pages 652–660, Springer, Cham., Oct, 2015.

J. Ferlay et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. International journal of cancer, 136(5):E359–E386, 2015.

T. F´evry et al. Improving localization-based approaches for breast cancer screening exam classification. In Proceedings of Machine Learning Research, Extended Abstract MIDL 2019.

M. A. Ganaie et al. Ensemble deep learning: A review, 2021. arXiv preprint arXiv:2104.02395v1.

K. J. Geras et al. High-resolution breast cancer screening with multi-view deep convolutional neural networks, 2017. arXiv preprint arXiv:1703.07047.

Gulum et al. A review of explainable deep learning cancer detection models in medical imaging. Applied Sciences, 11(10), 2021.

S. Hofvind et al. The Norwegian breast cancer screening program, 1996-2016: Celebrating 20 years of organised mammographic screening, 2017. In: Cancer in Norway 2016 - Cancer incidence, mortality, survival and prevalence in Norway. Oslo: Cancer Registry of Norway.

H. E. Kim et al. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. The Lancet Digital Health, 2.3:e138–e148, 2020.

T. Kooi and N. Karssemeijer. Classifying symmetrical differences and temporal change for the detection of malignant masses in mammography using deep neural networks. Journal of Medical Imaging, 4.4:044501, 2017.

B. Lauby-Secretan et al. Breast-cancer screening — viewpoint of the IARC working group. N Engl J Med, pages 372:2353–2358, 2015, DOI: 10.1056/NEJMsr1504363.

K. Liu et al. Weakly-supervised high-resolution segmentation of mammography images for breast cancer diagnosis, 2021. arXiv preprint arXiv:2106.07049.

S. M. McKinney et al. International evaluation of an AI system for breast cancer screening. Nature, 577(7788):89–94, 2020.

C. Mohammad et al. Breast cancer detection in mammograms using convolutional neural network. In 2018 International Conference on Computing, Mathematics and Engineering Technologies, 2018.

Y. Shen et al. Globally-aware multiple instance classifier for breast cancer screening. In International Workshop on Machine Learning in Medical Imaging, pages 18–26, Springer, Cham., 2019, October.

Y. Shen et al. An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization. Medical image analysis, 68:101908, 2021.

K. Simonyan et al. Deep inside convolutional networks: Visualising image classification models and saliency maps, 2014. arXiv:1312.6034.

Url. https://www.synapse.org/#!Synapse: syn4224222/wiki/401743.

X. Whang. Inconsistent performance of deep learning models on mammogram classification. Journal of the American College of Radiology, 17(6):796–803, 2020.

N. Wu et al. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE transactions on medical imaging, 39(4):1184–1194, 2019 Oct 7.

C. Zhang et al. New convolutional neural network model for screening and diagnosis of mammograms. PLOS ONE, 15(8), 2020.

W. Zhu et al. Deep multi-instance networks with sparse label assignment for whole mammogram classification. In Int. Conf. on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 2017.