ChestBioX-Gen: contextual biomedical report generation from chest X-ray images using BioGPT and co-attention mechanism
Bibliographic record
Abstract
Efficient and accurate radiology reporting is critical in modern healthcare for timely diagnosis and patient care. In this paper, we present a novel deep learning approach that leverages BioGPT and co-attention mechanisms for automatic chest X-ray report generation. Our model, termed “ChestBioX-Gen” is designed to bridge the gap between medical images and textual reports. BioGPT, a biological language model, contributes its contextual understanding to the task, while the co-attention mechanism efficiently aligns relevant regions of the image with textual descriptions. This collaborative combination enables ChestBioX-Gen to generate coherent and contextually accurate reports that embed complex medical findings. Our model not only reduces the burden on radiologists but also enhances the consistency and quality of reports. By automating the report generation process, ChestBioX-Gen contributes to faster diagnoses and improved patient care. Quantitative evaluations, measured through BLEU-N and Rouge-L metrics, demonstrate the model's proficiency in producing clinically relevant reports with scores of 0.6685, 0.6247, 0.5689, 0.4806, and 0.7742 on BLUE 1, 2, 3, 4, and Rouge-L, respectively. In conclusion, the integration of BioGPT and co-attention mechanisms in ChestBioX-Gen represents an advancement in AI-driven medical image analysis. As radiology reporting plays a critical role in healthcare, our model holds the potential to revolutionize how medical insights are extracted and communicated, ultimately benefiting both radiologists and patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".