Targeted generative data augmentation for automatic metastases detection from free-text radiology reports
Automatic identification of metastatic sites in cancer patients from electronic health records is a challenging yet crucial task with significant implications for diagnosis and treatment. In this study, we demonstrate how advancements in natural language processing, namely the instruction-following...
Saved in:
Main Authors: | Maede Ashofteh Barabadi, Xiaodan Zhu, Wai Yip Chan, Amber L. Simpson, Richard K. G. Do |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2025-02-01
|
Series: | Frontiers in Artificial Intelligence |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1513674/full |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Data Augmentation For Sorani Kurdish News Headline Classification Using Back-Translation And Deep Learning Model
by: Soran Badawi
Published: (2023-06-01) -
Understanding perception of the radiology community concerning virtual reality (VR) and augmented reality (AR) technology in radiology education
by: Suneet Paulson, et al.
Published: (2025-02-01) -
Large language models generating synthetic clinical datasets: a feasibility and comparative analysis with real-world perioperative data
by: Austin A. Barr, et al.
Published: (2025-02-01) -
A Comprehensive Survey of Fake Text Detection on Misinformation and LM-Generated Texts
by: Soonchan Kwon, et al.
Published: (2025-01-01) -
Into the latent space of capacitive sensors: interpolation and synthetic data generation using variational autoencoders
by: Miguel Monteagudo Honrubia, et al.
Published: (2025-01-01)