Accurate Landmark Localization for Medical Images Using Perturbations
Source abstract
Abstract
Recently, various studies have been proposed to learn the rich representations of images during deep learning. In particular, the perturbation method is a simple way to learn rich representations that has shown significant success. In this study, we present effective perturbation approaches for medical landmark localization. To this end, we report an extensive experiment that uses the perturbation methods of erasing, smoothing, binarization, and edge detection. The hand X-ray dataset and the ISBI 2015 Cephalometric dataset are used to evaluate the perturbation effect. The experimental results show that the perturbation method forces the network to extract richer representations of an image, leading to performance increases. Moreover, in comparison with the existing methods that lack any complex algorithmic change of network, our methods with specific perturbation methods achieve superior performance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Dental Radiography and Imaging · Medical Imaging and Analysis · AI in cancer detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Division of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, Korea
Department of Computer and Software Engineering, Wonkwang University, Iksan 54538, Korea
References (43)
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