Abstract
Traditionally, the analysis of histological samples is visually performed by a pathologist, who inspects under the microscope the tissue samples, looking for malignancies and anomalies. This visual assessment is both time consuming and highly unreliable due to the subjectivity of the evaluation. Hence, there are growing efforts towards the automatisation of such analysis, oriented to the development of computer-aided diagnostic tools, with a ever-growing role of techniques based on deep learning. In this work, we analyze some of the issues commonly associated with providing deep learning based techniques to medical professionals. We thus introduce a tool, aimed at both researchers and medical professionals, which simplifies and accelerates the training and exploitation of such models. The outcome of the tool is an attention map representing cancer probability distribution on top of the Whole Slide Image, driving the pathologist through a faster and more accurate diagnostic procedure.
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References
Zarella, M., et al.: A practical guide to whole slide imaging: a white paper from the digital pathology association. Arch. Pathol. Lab. Med. 143 (2018). https://doi.org/10.5858/arpa.2018-0343-RA
Pantanowitz, L., Farahani, N., Parwani, A.: Whole side imaging in pathology: advantage, limitations, and emerging perspectives. Pathol. Lab. Med. Int. 2015 (2015). https://doi.org/10.2147/PLMI.S59826
Xu, Y., et al.: Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. In: BMC Bioinformatics (2017)
Ponzio, F., et al.: Dealing with lack of training data for convolutional neural networks: the case of digital pathology. Electronics 8, 256 (2019). https://doi.org/10.3390/electronics8030256
Xing, F., et al.: Deep learning in microscopy image analysis: a survey. IEEE Trans. Neural Netw. Learn. Syst. 99, 1–19 (2017)
Lin, H., et al.: ScanNet: a fast and dense scanning framework for metastatic breast cancer detection from whole-slide images (2017). arXiv:1707.09597
Bray, F., et al.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Canc. J. Clin. 68(6), 394–424 (2018). https://doi.org/10.3322/caac.21492. https://onlinelibrary.wiley.com
Macenko, M., et al.: A method for normalizing histology slides for quantitative analysis. 9, 1107–1110 (2009). https://doi.org/10.1109/ISBI.2009.5193250
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv:1412.6980 [cs.LG]
Tustison, N.J., Gee, J.C.: Introducing Dice, Jaccard, and other label overlap measures to ITK. Insight. J. 2 (2009)
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Mascolini, A., Puzzo, S., Incatasciato, G., Ponzio, F., Ficarra, E., Di Cataldo, S. (2021). A Novel Proof-of-concept Framework for the Exploitation of ConvNets on Whole Slide Images. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Progresses in Artificial Intelligence and Neural Systems. Smart Innovation, Systems and Technologies, vol 184. Springer, Singapore. https://doi.org/10.1007/978-981-15-5093-5_12
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DOI: https://doi.org/10.1007/978-981-15-5093-5_12
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