Abstract
Colorectal cancer is one of the most widespread cancers in western countries. As with most forms of cancers, its diagnosis can only be made by performing a histopathological examination from tissue collected in the suspicious area. This process is time-consuming for the pathologist and consequently for the patient, whereas recent advances in computer vision and machine learning have shown that models can match the performance of human experts in classification and segmentation challenges. However the medical and ethical responsibilities that come with the use of artificial intelligence in healthcare demand that predictions are interpretable, and not just be the output of a black-box model. This paper demonstrates how visualisation of learned features play an important role in making the decision-making process transparent and helps to justify alignment with clinical features of colorectal cancer.
Tapabrata Chakraborti and Jens Rittscher are joint senior authors for this paper. The work was done with funding from the 2020 Oxford Engineering French Internship Scheme. For the publication, TC (corresponding author) is sponsored by the Turing-Roche Strategic Partnership.
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References
CTC of America (2020) Colorectal cancer types. https://www.cancercenter.com/cancer-types/colorectal-cancer/types. Accessed 14 Sep 2020
Bianco S, Cadene R, Celona L, Napoletano P (2018) Benchmark analysis of representative deep neural network architectures. IEEE Access 6:64270–64277. https://doi.org/10.1109/ACCESS.2018.2877890
Chollet F (2015) Keras. https://github.com/fchollet/keras
Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 31(4–5):198–211. https://doi.org/10.1016/j.compmedimag.2007.02.002
Gupta P, Huang Y, Sahoo PK, You JF, Chiang SF, Onthoni DD, Chern YJ, Chao KY, Chiang JM, Yeh CY, Tsai WS (2021) Colon tissues classification and localization in whole slide images using deep learning. Diagnostics 11(8):1398
Hanahan D, Weinberg RA (2000) The hallmarks of cancer. Cell 100(1):57–70. https://doi.org/10.1016/s0092-8674(00)81683-9
Kather JN, Halama N, Marx A (2018) 100,000 histological images of human colorectal cancer and healthy tissue. https://doi.org/10.5281/zenodo.1214456
Keys RG (1981) Cubic convolution interpolation for digital image processing. IEEE Trans Acoust Speech Signal Process 29(6):1153–1160. https://doi.org/10.1109/TASSP.1981.1163711
Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. In: 3rd international conference on learning representations, ICLR 2015-conference track proceedings, pp 1–15
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252. https://doi.org/10.1007/s11263-015-0816-y
Satyanarayanan M, Goode A, Gilbert B, Harkes J, Jukic D (2013) OpenSlide: a vendor-neutral software foundation for digital pathology. J Pathol Inform 4(1):27. https://doi.org/10.4103/2153-3539.119005
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2020) Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vis 128(2):336–359. https://doi.org/10.1007/s11263-019-01228-7
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: 3rd international conference on learning representations, ICLR 2015-conference track proceedings, pp 1–14
UK CR (2017) Cancer statistics for the UK. https://www.cancerresearchuk.org/health-professional/cancer-statistics-for-the-uk. Accessed 12 Sep 2020
Yu G, Sun K, Xu C, Shi XH, Wu C, **e T, Meng RQ, Meng XH, Wang KS, **ao HM, Deng HW (2021) Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images. Nat Commun 12(1):6311
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Guérin, A., Basu, S., Chakraborti, T., Rittscher, J. (2023). Deep Visualisation-Based Interpretable Analysis of Digital Pathology Images for Colorectal Cancer. In: Sarkar, R., Pal, S., Basu, S., Plewczynski, D., Bhattacharjee, D. (eds) Proceedings of International Conference on Frontiers in Computing and Systems. COMSYS 2022. Lecture Notes in Networks and Systems, vol 690. Springer, Singapore. https://doi.org/10.1007/978-981-99-2680-0_49
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