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.