Kim, YongSoo Park, Seungbin Kim, Hannah Kim, Seung-seob Lim, JoonSeok Kim, Sungwon Choi, Kihwan Seo, Hyun seok 2024-01-12T03:41:04Z 2024-01-12T03:41:04Z 2022-06-15 2022-07-14 https://pubs.kist.re.kr/handle/201004/77148 https://ieeexplore.ieee.org/abstract/document/9871285 The field of medical image analysis has been attracted to deep learning. Various deep learning-based techniques have been introduced to aid diagnosis in the CT image of the patient. The auxiliary model for diagnosis that we proposed is to detect colorectal tumors in the CT image. The model is combined with two contrary networks of ‘Detection Transformer’ and ‘Hourglass’. Furthermore, to improve the performance of the model, we propose an efficient connection method for two contrary models by using intermediate prediction information. A total of 3,509 patients (193,567 CT images) were applied to the experiment and our model outperforms the conventional models in colorectal tumor detection. English IEEE Engineering in Medicine and Biology Society A Bounding-Box Regression Model for Colorectal Tumor Detection in CT Images Via Two Contrary Networks Conference 10.1109/EMBC48229.2022.9871285 1 44th International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 44th International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) SC Glasgow 2022-07-11 44th International Conference of the IEEE Engineering in Medicine & Biology Society