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Information extraction from weakly structured radiological reports with natural language queries paper by Independent / Community
⭐ 66.4
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Paper

Information extraction from weakly structured radiological reports with natural language queries

by Independent / Community 00f09c67d70f66526d781791773ad3214a00833f
Free2AITools Nexus Index
66.4
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 78
P: Popularity 53
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

Objectives Provide physicians and researchers an efficient way to extract information from weakly structured radiology reports with natural language processing (NLP) machine learning models. Methods We evaluate seven different German bidirectional encoder representations from transformers (BERT) models on a dataset of 857,783 unlabeled radiology reports and an annotated reading comprehension dataset in the format of SQuAD 2.0 based on 1223 additional reports. Results Continued pre-training of...

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Semantic Scholar 15 Citations
Paper Information Summary
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Registry ID 00f09c67d70f66526d781791773ad3214a00833f
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{00f09c67d70f66526d781791773ad3214a00833f,
  author = {Unknown},
  title = {Information extraction from weakly structured radiological reports with natural language queries Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/00f09c67d70f66526d781791773ad3214a00833f}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). Information extraction from weakly structured radiological reports with natural language queries [Paper]. Free2AITools. https://api.semanticscholar.org/00f09c67d70f66526d781791773ad3214a00833f

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Full Specifications [+]

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 78
Popularity (P) 53
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Information extraction from weakly structured radiological reports with natural language queries: Authority (A:78), Popularity (P:53), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data

πŸ“ Executive Summary

"Objectives Provide physicians and researchers an efficient way to extract information from weakly structured radiology reports with natural language processing (NLP) machine learning models. Methods We evaluate seven different German bidirectional encoder representations from transformers (BERT) models on a dataset of 857,783 unlabeled radiology reports and an annotated reading comprehension dataset in the format of SQuAD 2.0 based on 1223 additional reports. Results Continued pre-training of..."

❝ Cite Node 

@article{Unknown2026Information,
  title={Information extraction from weakly structured radiological reports with natural language queries},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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πŸ“Š Research Signals

πŸ“ˆ15CitationsSemantic Scholar
πŸ›οΈ78AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈautomation workflowField

🏷️ Research Topics

transformer architecture
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semantic_scholar
author
Unknown
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

architecture
null
params billions
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