Abstract
Machine learning (ML) and especially deep learning (DL) are doubtless one of the key technologies of the last couple of years and future decades. Learning its theoretical concepts alone is a big challenge as it requires a strong background in mathematics and computer science. Once students and researchers have built up on the core concepts and recent algorithms using toy data sets and want to move forward tackling real-world application scenarios, they are confronted with additional problems, specific to the application domain. Digital pathology is one of those domains that has many of these additional issues, such as: New file formats, large images, and the amount of medical expertise needed to capture the underlying problem is relatively large. In our project deep-teaching.org we provide teaching materials to introduce computer science students to the use of ML for digital pathology. As example, we present the application of pathologic N-stage (pN-stage) classification by dividing it into sub-tasks and propose - step by step - how different machine and deep learning, as well as computer vision techniques, can be combined to contribute to the main objective.
Supported by the German Ministry of Education and Research (BMBF), project number 01IS17056.
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References
Background section of the hompage of the Camelyon17 challenge. https://camelyon17.grand-challenge.org/background/. Accessed 18 Nov 2019
Data section of the hompage of the Camelyon17 challenge. https://camelyon17.grand-challenge.org/evaluation/. Accessed 18 Nov 2019
Definition of TNM System, Website of the National Cancer Institute. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/tnm-staging-system. Accessed 18 Nov 2019
Evaluation section of the hompage of the Camelyon17 challenge. https://camelyon17.grand-challenge.org/evaluation/. Accessed 18 Nov 2019
Glossary on the hompage of the Digital Pathology Association. https://digitalpathologyassociation.org/glossary-of-terms_1. Accessed 18 Nov 2019
Hegemann, L.: Wenn Politik auf künstliche Intelligenz trifft. In: Zeit Online (2018). https://www.zeit.de/digital/internet/2018-11/digitalisierung-ki-strategie-investitionen-bundesregierung. Accessed 18 Nov 2019
Hompage of Drew Conway. http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram. Accessed 04 Nov 2019
Lee, B., Paeng, K.: Breast Cancer Stage Classification in Histopathology Images. In: Submission results Camelyon17 challange. https://camelyon17.grand-challenge.org/evaluation/results/. Accessed 18 Nov 2019
Lee, S., Oh, S., Choi, K., Kim, S.: Automatic classification on patient-level breast cancer metastases. In: Submission results Camelyon17 challange. https://camelyon17.grand-challenge.org/evaluation/results/. Accessed 18 Nov 2019
Lewis, W.: Skype translator: Breaking down language and hearing barriers. In: Translating and the Computer (TC37), London, pp. 125–149 (2015)
Migration guide of the Tensorflow hompage. https://www.tensorflow.org/guide/migrate/. Accessed 19 Nov 2019
Openslide homepage. https://openslide.org/. Accessed 18 Nov 2019
Pinchaud, N., Hedlund, M.: Camelyon17 grand challenge. In: Submission results Camelyon17 challange. https://camelyon17.grand-challenge.org/evaluation/results/. Accessed 18 Nov 2019
Quick reference on cancer staging - Hompage of the American Joint Committee on Cancer. https://cancerstaging.org/references-tools/quickreferences/Documents/BreastMedium.pdf. Accessed 18 Nov 2019
Results of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012. http://image-net.org/challenges/LSVRC/2012/results.html. Accessed 01 Nov 2019
Results section of the hompage of the Camelyon17 challenge. https://camelyon17.grand-challenge.org/evaluation/results/. Accessed 18 Nov 2019
Strohmenger, K., Annuscheit, J., Klempert, I., Voigt, B., Herta, C., Hufnagl, P.: Convolutional neural networks and random forests for detection and classification of metastasis in histological slides. In: Submission results Camelyon17 challange. https://camelyon17.grand-challenge.org/evaluation/results/. Accessed 18 Nov 2019
Unkown Author (alias Ozymandias): AI breast cancer detection. In: Submission results Camelyon17 challange. https://camelyon17.grand-challenge.org/evaluation/results/. Accessed 18 Nov 2019
Zhao, Z., Lin, H., Heng, P.: Breat Cancer pN-Stage classification for whole slide images. In: Submission results Camelyon17 challange. https://camelyon17.grand-challenge.org/evaluation/results/. Accessed 18 Nov 2019
Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017). https://doi.org/10.1038/nature21056
Folk, M., Heber, G., Koziol, Q., Pourmal, E., Robinson, D.: An overview of the HDF5 technology suite and its applications. In: Proceedings of the EDBT/ICDT 2011 Workshop on Array Databases, AD 2011, pp. 36–47. ACM, New York (2011). https://doi.org/10.1145/1966895.1966900, http://doi.acm.org/10.1145/1966895.1966900
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, June 2016. https://doi.org/10.1109/CVPR.2016.90
Holzinger, A., et al.: Towards the augmented pathologist: challenges of explainable-AI in digital pathology (2017)
Lin, H., Chen, H., Dou, Q., Wang, L., Qin, J., Heng, P.: Scannet: a fast and dense scanning framework for metastastic breast cancer detection from whole-slide image. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 539–546, March 2018. https://doi.org/10.1109/WACV.2018.00065
Magee, D., et al.: Colour normalisation in digital histopathology images. In: Proceedings Optical Tissue Image analysis in Microscopy, Histopathology and Endoscopy (MICCAI Workshop), January 2009
Parvat, A., Chavan, J., Kadam, S., Dev, S., Pathak, V.: A survey of deep-learning frameworks. In: 2017 International Conference on Inventive Systems and Control (ICISC), pp. 1–7, January 2017. https://doi.org/10.1109/ICISC.2017.8068684
Roy, S., kumar, J.A., Lal, S., Kini, J.: A study about color normalization methods for histopathology images. Micron 114, 42–61 (2018). https://doi.org/10.1016/j.micron.2018.07.005. http://www.sciencedirect.com/science/article/pii/S0968432818300982
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y
Satyanarayanan, M., Goode, A., Gilbert, B., Harkes, J., Jukic, D.: OpenSlide: a vendor-neutral software foundation for digital pathology. J. Pathol. Inform. 4(1), 27 (2013). https://doi.org/10.4103/2153-3539.119005. https://doi.org/10.4103%2F2153-3539.119005
Shin, H., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016). https://doi.org/10.1109/TMI.2016.2528162
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Strohmenger, K., Herta, C., Fischer, O., Annuscheit, J., Hufnagl, P. (2020). Higher Education Teaching Material on Machine Learning in the Domain of Digital Pathology. In: Holzinger, A., Goebel, R., Mengel, M., Müller, H. (eds) Artificial Intelligence and Machine Learning for Digital Pathology. Lecture Notes in Computer Science(), vol 12090. Springer, Cham. https://doi.org/10.1007/978-3-030-50402-1_10
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