Partial Objective Weighting in Cone Beam CT Reports
A new study introduces a method for generating maxillofacial reports from cone beam computed tomography (CBCT) images, scoring them with a composite objective that gives 80% weight to factual entailment and 20% to lexical overlap. The system uses a fine-tuned large language model (LLM), achieving an AUC of 0.486 over 985 statements in the public release dataset. Key metrics include 0.945 for…
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Key points
- Composite objective scores CBCT reports with 80% factual entailment and 20% lexical overlap
- Fine-tuned LLM achieves AUC of 0.486 over 985 statements in the public release
- AUCs for mandible (0.945) and condyle (0.872) coverage are reported
Read the original at arXiv cs.CL · by Ajo Babu George, Govind Arun, Sidharth N Krishna, Uma Ranjan primary source Open source ↗
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