2021/09/26 by Yibao Sun, Giussepi Lopez, Sun, Yibao +9
Computer Science · Engineering · Medicine · #AI in cancer detection #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Medical Image Segmentation Techniques #Radiomics and Machine Learning in Medical Imaging #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2109.12617
arxiv created 2021/09/26 · openalex publication_date 2021/09/26 · arxiv updated 2021/09/28 · openalex created_date 2021/10/11 · openalex updated_date 2026/07/28
Cancer segmentation in whole-slide images is a fundamental step for viable tumour burden estimation, which is of great value for cancer assessment. However, factors like vague boundaries or small regions dissociated from viable tumour areas make it a challenging task. Considering the usefulness of multi-scale features in various vision-related tasks, we present a structure-aware scale-adaptive feature selection method for efficient and accurate cancer segmentation. Based on a segmentation network with a popular encoder-decoder architecture, a scale-adaptive module is proposed for selecting more robust features to represent the vague, non-rigid boundaries. Furthermore, a structural similarity metric is proposed for better tissue structure awareness to deal with small region segmentation. In addition, advanced designs including several attention mechanisms and the selective-kernel convolutions are applied to the baseline network for comparative study purposes. Extensive experimental results show that the proposed structure-aware scale-adaptive networks achieve outstanding performance on liver cancer segmentation when compared to top ten submitted results in the challenge of PAIP 2019. Further evaluation on colorectal cancer segmentation shows that the scale-adaptive module improves the baseline network or outperforms the other excellent designs of attention mechanisms when considering the tradeoff between efficiency and accuracy.