Authors:
Nelson Faria
1
;
Sofia Campelos
2
and
Vítor Carvalho
1
Affiliations:
1
12Ai - School of Technology, IPCA, Barcelos, Portugal
;
2
Pathology Laboratory, IPATIMUP - Institute of Pathology and Molecular Immunology, University of Porto, Porto, Portugal
Keyword(s):
Lung Cancer, Digital Pathology, Deep Learning, Convolutional Neural Networks, Whole-Slide Imaging.
Abstract:
Lung cancer is the type of cancer that causes most deaths worldwide and as sooner it is discovered as more possibilities there are for the patient to be treated. An accurate histological classification of tumours is essential for lung cancer diagnosis and adequate patient management. Whole-slide images (WSI) generated from tissue samples can be analysed using Deep Learning techniques to assist pathologists. In this study it is given an overview of the lung cancer exploring the different types of implementations undertaken until the present. These methods show a two-step implementation in which the tasks consist primarily of the detection of the tumour and after on the histologic classification of the tumour. To detect the neoplastic cells, the WSI is split in patches, and then a convolutional neural network is applied to identify and generate a heatmap highlighting the tumour regions. In the next step, features are extracted from the neoplasic regions and submitted in a classifier to
determine the histologic type of tumour present in each patch. Moreover, in this paper, it is proposed a possible approach based on the literature review to surpass the limitations found in the actual models, and with better performance and accuracy, that could be used as an aid in the pathological diagnosis of the lung cancer.
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