Name
Title | ||
|---|---|---|
A Bounding-Box Regression Model for Colorectal Tumor Detection in CT Images Via Two Contrary Networks. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/EMBC48229.2022.9871285 | Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
DocType | Volume | ISSN |
Conference | 2022 | 2694-0604 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| YongSoo Kim | 1 | 0 | 0.34 |
| Seungbin Park | 2 | 0 | 0.34 |
| Hannah Kim | 3 | 1 | 2.51 |
| Seung-Seob Kim | 4 | 0 | 0.34 |
| Joon Seok Lim | 5 | 0 | 0.34 |
| Sungwon Kim | 6 | 0 | 0.68 |
| Kihwan Choi | 7 | 0 | 0.68 |
| Hyunseok Seo | 8 | 0 | 0.34 |