Multimodal Deep Learning Methods on Image and Textual Data to Predict Radiotherapy Structure Names
Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard random names. Hence, the standardization of these names for the Organs at Risk (OARs), Planning Target Volumes (PTVs), and ‘Other’ organs is a vital problem. This paper...
Main Authors: | Priyankar Bose, Pratip Rana, William C. Sleeman, Sriram Srinivasan, Rishabh Kapoor, Jatinder Palta, Preetam Ghosh |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-06-01
|
Series: | BioMedInformatics |
Subjects: | |
Online Access: | https://www.mdpi.com/2673-7426/3/3/34 |
Similar Items
-
A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts
by: Priyankar Bose, et al.
Published: (2021-09-01) -
Multi-View Data Integration Methods for Radiotherapy Structure Name Standardization
by: Khajamoinuddin Syed, et al.
Published: (2021-04-01) -
Integrated Natural Language Processing and Machine Learning Models for Standardizing Radiotherapy Structure Names
by: Khajamoinuddin Syed, et al.
Published: (2020-04-01) -
Attention-Based Multimodal Deep Learning on Vision-Language Data: Models, Datasets, Tasks, Evaluation Metrics and Applications
by: Priyankar Bose, et al.
Published: (2023-01-01) -
Visual Clue Guidance and Consistency Matching Framework for Multimodal Named Entity Recognition
by: Li He, et al.
Published: (2024-03-01)