2017/08/11 by Jung Uk Kim, Kim, Jung Uk, Hak Gu Kim +3
Computer Science · Medicine · #AI in cancer detection #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Medical Image Segmentation Techniques #Radiomics and Machine Learning in Medical Imaging
paper · pdf · doi:10.48550/arxiv.1708.03431
openalex publication_date 2017/08/11 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
In this paper, we propose a novel medical image segmentation using iterative\ndeep learning framework. We have combined an iterative learning approach and an\nencoder-decoder network to improve segmentation results, which enables to\nprecisely localize the regions of interest (ROIs) including complex shapes or\ndetailed textures of medical images in an iterative manner. The proposed\niterative deep convolutional encoder-decoder network consists of two main\npaths: convolutional encoder path and convolutional decoder path with iterative\nlearning. Experimental results show that the proposed iterative deep learning\nframework is able to yield excellent medical image segmentation performances\nfor various medical images. The effectiveness of the proposed method has been\nproved by comparing with other state-of-the-art medical image segmentation\nmethods.\n