A deep learning-based method for relative location prediction in CT scan images
This addresses a domain-specific challenge in medical imaging for improving CT scan analysis, but it appears incremental as it builds on existing deep learning methods.
The paper tackled the problem of relative location prediction in CT scan images by proposing a regression model based on one-dimensional convolutional neural networks, achieving a median absolute error of 1.04 cm and mean absolute error of 1.69 cm.
Relative location prediction in computed tomography (CT) scan images is a challenging problem. In this paper, a regression model based on one-dimensional convolutional neural networks is proposed to determine the relative location of a CT scan image both robustly and precisely. A public dataset is employed to validate the performance of the study's proposed method using a 5-fold cross validation. Experimental results demonstrate an excellent performance of the proposed model when compared with the state-of-the-art techniques, achieving a median absolute error of 1.04 cm and mean absolute error of 1.69 cm.