Deep learning and radiomics for gastric cancer serosal invasion: automated segmentation and multi-machine learning from two centers
Abstract Objective The objective of this study is to develop an automated method for segmenting spleen computed tomography (CT) images using a deep learning model. This approach is intended to address the limitations of manual segmentation, which is known to be susceptible to inter-observer variabil...
Saved in:
Main Authors: | Hui Shang, Tao Feng, Dong Han, Fengying Liang, Bin Zhao, Lihang Xu, Zhendong Cao |
---|---|
Format: | Article |
Language: | English |
Published: |
Springer
2025-02-01
|
Series: | Journal of Cancer Research and Clinical Oncology |
Subjects: | |
Online Access: | https://doi.org/10.1007/s00432-025-06117-w |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Integrating manual annotation with deep transfer learning and radiomics for vertebral fracture analysis
by: Jing Wang, et al.
Published: (2025-02-01) -
Machine learning assisted radiomics in predicting postoperative occurrence of deep venous thrombosis in patients with gastric cancer
by: Yuan Zeng, et al.
Published: (2025-02-01) -
Gastric Cytoprotection as Basis of Gastrointestinal Mucosa Protection and Repair in Erosive Ulcerative Lesions of Various Aetiologies
by: K. V. Ivashkin, et al.
Published: (2020-12-01) -
Ulnar variance detection from radiographic images using deep learning
by: Sahar Nooh, et al.
Published: (2025-02-01) -
Analysis of Risk Factors for Postoperative Progressive Segment Degeneration at the Decompression and Non-decompression Segments after Minimally Invasive Lumbar Decompression Surgery: A 5-year Follow-up Study
by: Hasibullah Habibi, et al.
Published: (2025-01-01)