Convolutional Neural Networks for Classification of Colorectal Cancer on Whole Slide Images

2021-03-27
Temena, Mehmet Arda
Isildak, Ulas
Acar, Ahmet
Diagnosis of cancer is typically performed by medical pathologist visually inspecting hematoxylin and eosin (H&E) stained whole slide images. Although the early detection of various cancer types is crucial for a successful treatment, it is a challenging work due to inter- and intra-personal variability and it may often result in a disagreement between pathologists. Visual inspection of medical specimen is a well- established method for cancer diagnosis, but digital transformation is now inevitable fact, whereby conventional microscopic examinations with bareeyes have been replaced by artificial learning (AI)- based digital pathology applications in last years. For this reason, AI-driven solutions are now helping to operate on biospecimen images mostly to assist pathologists. Moreover, deep learning-based diagnosis systems have showed a potential for reducing the cost and improving the accuracy. In this work, we aimed to develop a automated pipeline for colorectal cancer diagnoses by applying Convolutional Neural Network (CNN) based models to classify whole slide images in two classes, carcinoma and non-carcinoma. We included several previously developed deep learning classifiers in our pipeline, where a data set with more than 300 annotated and accessible whole-slide images for colorectal cancer will be used to evaluate model performances.

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Citation Formats
M. A. Temena, U. Isildak, and A. Acar, “Convolutional Neural Networks for Classification of Colorectal Cancer on Whole Slide Images,” presented at the 13th CONGRESS of MEDICAL INFORMATICS, Ankara, Türkiye, 2021, Accessed: 00, 2021. [Online]. Available: https://turkmia.net/wp-content/uploads/2021/03/TURKMIA2021-Proceedings.pdf.