2020/07/25 by Yanbo Feng, Feng, Yanbo, Adel Hafiane +3
Computer Science · Engineering · Medicine · #AI in cancer detection #Artificial intelligence #Cancer #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Deep learning #FOS: Computer and information sciences #FOS: Electrical engineering #Image (mathematics) #Image and Video Processing (eess.IV) #Internal medicine #Liver cancer #Machine Learning (cs.LG) #Medical Image Segmentation Techniques #Medicine #Pattern recognition (psychology) #Radiomics and Machine Learning in Medical Imaging #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2007.12935
arxiv created 2020/07/25 · openalex publication_date 2020/07/25 · arxiv updated 2020/07/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper addresses the problem of liver cancer segmentation in Whole Slide Image (WSI). We propose a multi-scale image processing method based on automatic end-to-end deep neural network algorithm for segmentation of cancer area. A seven-levels gaussian pyramid representation of the histopathological image was built to provide the texture information in different scales. In this work, several neural architectures were compared using the original image level for the training procedure. The proposed method is based on U-Net applied to seven levels of various resolutions (pyramidal subsumpling). The predictions in different levels are combined through a voting mechanism. The final segmentation result is generated at the original image level. Partial color normalization and weighted overlapping method were applied in preprocessing and prediction separately. The results show the effectiveness of the proposed multi-scales approach achieving better scores compared to the state-of-the-art.