Seo, Hyunseok So, Seohee Yun, Sojin Lee, Seokjun Barg, Jiseong 2024-01-12T02:49:40Z 2024-01-12T02:49:40Z 2022-11-10 2022-09-18 0302-9743 https://pubs.kist.re.kr/handle/201004/76609 Target delineation in the medical images can be utilized in lots of clinical applications, such as computer-aided diagnosis, prognosis, or radiation treatment planning. Deep learning has tremendously improved the performances of automated segmentation in a data-driven manner as compared with conventional machine learning models. In this work, we propose a spatial feature conservative design for feature extraction in deep neural networks. To avoid signal loss from sub-sampling of the max pooling operations, multi-scale dilated convolutions are applied to reach the large receptive field. Then, we propose a novel compensation module that prevents intrinsic signal loss from dilated convolution kernels. Furthermore, an adaptive combination method of the dilated convolution results is devised to enhance learning efficiency. The proposed model is validated on the delineation of breast cancer in DCE-MR images obtained from public dataset. The segmentation results clearly show that the proposed network model provides the most accurate delineation results of the breast cancers in the DCE-MR images. The proposed model can be applied to other clinical practice sensitive to spatial information loss. English SPRINGER INTERNATIONAL PUBLISHING AG Spatial Feature Conservation Networks (SFCNs) for Dilated Convolutions to Improve Breast Cancer Segmentation from DCE-MRI Conference 10.1007/978-3-031-17721-7_13 1 1st International Workshop on Applications of Medical Artificial Intelligence (AMAI), pp.118 - 127 1st International Workshop on Applications of Medical Artificial Intelligence (AMAI) 118 127 SZ Singapore, SINGAPORE 2022-09-18 APPLICATIONS OF MEDICAL ARTIFICIAL INTELLIGENCE, AMAI 2022 000870091500013