Keiju Aokage

According to our database1, Keiju Aokage authored at least 8 papers between 2014 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

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PhD thesis 
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Links

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Bibliography

2023
Representation of thoracic N1 lymph nodes group in contrast-enhanced CT images using distance maps based on tracheobronchial labeling.
Proceedings of the Medical Imaging 2023: Biomedical Applications in Molecular, 2023

2021
Representation of texture structures with topological data analysis for stage IA lung adenocarcinoma in three-dimensional thoracic CT images.
Proceedings of the Medical Imaging 2021: Biomedical Applications in Molecular, 2021

2020
A preliminary study of visualizing texture components of stage IA lung adenocarcinoma in three-dimensional thoracic CT images with structure-texture image decomposition.
Proceedings of the Medical Imaging 2020: Biomedical Applications in Molecular, 2020

2019
Computer-aided CT image features improving the malignant risk prediction in pulmonary nodules suspicious for lung cancer.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019

2018
Prognostic importance of pleural attachment status measured by pretreatment CT images in patients with stage IA lung adenocarcinoma: Measurement of the ratio of the interface between nodule and neighboring pleura to nodule surface area.
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018

2016
Preliminary study of visualizing membrane structures of spiculated pulmonary nodules in three-dimensional thoracic CT images.
Proceedings of the Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, San Diego, California, United States, 27 February, 2016

2015
Nonlinear dimensionality reduction of CT histogram based feature space for predicting recurrence-free survival in non-small-cell lung cancer.
Proceedings of the Medical Imaging 2015: Computer-Aided Diagnosis, 2015

2014
Potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection.
Proceedings of the Medical Imaging 2014: Computer-Aided Diagnosis, 2014


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