  Purpose: This study focuses on the development of automated text generation
from radiology images, termed diagnostic captioning, to assist medical
professionals in reducing clinical errors and improving productivity. The aim
is to provide tools that enhance report quality and efficiency, which can
significantly impact both clinical practice and deep learning research in the
biomedical field. Methods: In our participation in the ImageCLEFmedical2024
Caption evaluation campaign, we explored caption prediction tasks using
advanced Transformer-based models. We developed methods incorporating
Transformer encoder-decoder and Query Transformer architectures. These models
were trained and evaluated to generate diagnostic captions from radiology
images. Results: Experimental evaluations demonstrated the effectiveness of our
models, with the VisionDiagnostor-BioBART model achieving the highest BERTScore
of 0.6267. This performance contributed to our team, DarkCow, achieving third
place on the leaderboard. Conclusion: Our diagnostic captioning models show
great promise in aiding medical professionals by generating high-quality
reports efficiently. This approach can facilitate better data processing and
performance optimization in medical imaging departments, ultimately benefiting
healthcare delivery.
