TY - GEN
T1 - A Bounding-Box Regression Model for Colorectal Tumor Detection in CT Images Via Two Contrary Networks
AU - Kim, Yong Soo
AU - Park, Seungbin
AU - Kim, Hannah
AU - Kim, Seung Seob
AU - Lim, Joon Seok
AU - Kim, Sungwon
AU - Choi, Kihwan
AU - Seo, Hyunseok
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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. Clinical Relevance - The proposed model in this paper automatically detects colorectal tumors and provides the bounding box in the CT images. Colorectal tumor is one of the common diseases. In addition, the mortality rate is so high that in-time treatment is required. The model we present here has a sensitivity (or recall) of 84.73 % for tumor detection and a precision of 88.25 % in the patient CT data. The in-slice performance of the tumor detection shows an IoU of 0.56, a sensitivity of 0.67, and a precision of 0.68.
AB - 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. Clinical Relevance - The proposed model in this paper automatically detects colorectal tumors and provides the bounding box in the CT images. Colorectal tumor is one of the common diseases. In addition, the mortality rate is so high that in-time treatment is required. The model we present here has a sensitivity (or recall) of 84.73 % for tumor detection and a precision of 88.25 % in the patient CT data. The in-slice performance of the tumor detection shows an IoU of 0.56, a sensitivity of 0.67, and a precision of 0.68.
UR - http://www.scopus.com/inward/record.url?scp=85138128539&partnerID=8YFLogxK
U2 - 10.1109/EMBC48229.2022.9871285
DO - 10.1109/EMBC48229.2022.9871285
M3 - Conference contribution
C2 - 36085607
AN - SCOPUS:85138128539
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3793
EP - 3796
BT - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Y2 - 11 July 2022 through 15 July 2022
ER -
