Cargando…
Assessment of the Robustness of Convolutional Neural Networks in Labeling Noise by Using Chest X-Ray Images From Multiple Centers
BACKGROUND: Computer-aided diagnosis on chest x-ray images using deep learning is a widely studied modality in medicine. Many studies are based on public datasets, such as the National Institutes of Health (NIH) dataset and the Stanford CheXpert dataset. However, these datasets are preprocessed by c...
Autores principales: | Jang, Ryoungwoo, Kim, Namkug, Jang, Miso, Lee, Kyung Hwa, Lee, Sang Min, Lee, Kyung Hee, Noh, Han Na, Seo, Joon Beom |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7435602/ https://www.ncbi.nlm.nih.gov/pubmed/32749222 http://dx.doi.org/10.2196/18089 |
Ejemplares similares
-
A Curriculum Learning Strategy to Enhance the Accuracy of Classification of Various Lesions in Chest-PA X-ray Screening for Pulmonary Abnormalities
por: Park, Beomhee, et al.
Publicado: (2019) -
Reproducibility of abnormality detection on chest radiographs using convolutional neural network in paired radiographs obtained within a short-term interval
por: Cho, Yongwon, et al.
Publicado: (2020) -
CheSS: Chest X-Ray Pre-trained Model via Self-supervised Contrastive Learning
por: Cho, Kyungjin, et al.
Publicado: (2023) -
Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network
por: Jang, Miso, et al.
Publicado: (2023) -
Changes in chest X-ray findings in 1- and 2-month group after treatment initiation for suspected pulmonary tuberculosis
por: Lee, Jang Ho, et al.
Publicado: (2020)