Cargando…
Cascaded-Recalibrated Multiple Instance Deep Model for Pathologic-Level Lung Cancer Prediction in CT Images
Lung cancer accounts for the greatest number of cancer-related mortality, while the accurate evaluation of pulmonary nodules in computed tomography (CT) images can significantly increase the 5-year relative survival rate. Despite deep learning methods that have recently been introduced to the identi...
Autores principales: | Wang, Qingfeng, Zhou, Ying, Huang, Jun, Liu, Zhiqin, Zhang, Weidong, Liu, Qiyu, Cheng, Jie-Zhi |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9208922/ https://www.ncbi.nlm.nih.gov/pubmed/35733559 http://dx.doi.org/10.1155/2022/9469234 |
Ejemplares similares
-
Deep multiple instance learning for predicting chemotherapy response in non-small cell lung cancer using pretreatment CT images
por: Chang, Runsheng, et al.
Publicado: (2022) -
Lung cancer diagnosis using deep attention‐based multiple instance learning and radiomics
por: Chen, Junhua, et al.
Publicado: (2022) -
Classifying and segmenting microscopy images with deep multiple instance learning
por: Kraus, Oren Z., et al.
Publicado: (2016) -
Deep multiple-instance learning accurately predicts gene haploinsufficiency and deletion pathogenicity
por: Liu, Zhihan, et al.
Publicado: (2023) -
An Instance of Condensed Lungs
Publicado: (1808)