2020/02/12 by Mehmet Burak Sayıcı, Sayıcı, Mehmet Burak, Rikiya Yamashita +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Infrared Thermography in Medicine #Machine Learning (cs.LG) #Spectroscopy Techniques in Biomedical and Chemical Research #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2002.04836
This paper has been withdrawn by the authors due to need for heavy revise
openalex publication_date 2020/02/12 · arxiv created 2020/02/18 · arxiv updated 2020/02/20 · openalex created_date 2024/04/11 · openalex updated_date 2026/07/28
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. Whole-slide imaging which is a method of scanning glass slides have been employed for diagnosis of HCC. Using high resolution Whole-slide images is infeasible for Convolutional Neural Network applications. Hence tiling the Whole-slide images is a common methodology for assigning Convolutional Neural Networks for classification and segmentation. Determination of the tile size affects the performance of the algorithms since small field of view can not capture the information on a larger scale and large field of view can not capture the information on a cellular scale. In this work, the effect of tile size on performance for classification problem is analysed. In addition, Multi Field of View CNN is assigned for taking advantage of the information provided by different tile sizes and Attention CNN is assigned for giving the capability of voting most contributing tile size. It is found that employing more than one tile size significantly increases the performance of the classification by 3.97% and both algorithms are found successful over the algorithm which uses only one tile size.