# A Survey on Biomedical Image Captioning

arXiv: https://arxiv.org/abs/1905.13302 · HTML: https://mlsanity.com/paper/1905.13302/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2019.

## Built on by (most cited first, 30 of 30)

- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks](https://mlsanity.com/paper/2305.17100/) (2023)
- [Translation between Molecules and Natural Language](https://mlsanity.com/paper/2204.11817/) (2022)
- [Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training](https://mlsanity.com/paper/2210.07688/) (2022)
- [Deep Learning Approaches on Image Captioning: A Review](https://mlsanity.com/paper/2201.12944/) (2022)
- [Automated Radiology Report Generation: A Review of Recent Advances](https://mlsanity.com/paper/2405.10842/) (2024)
- [Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.08826/) (2025)
- [CLIP in Medical Imaging: A Survey](https://mlsanity.com/paper/2312.07353/) (2023)
- [Medical Image Captioning via Generative Pretrained Transformers](https://mlsanity.com/paper/2209.13983/) (2022)
- [Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2412.02621/) (2024)
- [Large-scale Long-tailed Disease Diagnosis on Radiology Images](https://mlsanity.com/paper/2312.16151/) (2023)
- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [Natural Language Processing for Smart Healthcare](https://mlsanity.com/paper/2110.15803/) (2021)
- [A Systematic Review of Deep Learning-based Research on Radiology Report Generation](https://mlsanity.com/paper/2311.14199/) (2023)
- [3D-MIR: A Benchmark and Empirical Study on 3D Medical Image Retrieval in Radiology](https://mlsanity.com/paper/2311.13752/) (2023)
- [UMIT: Unifying Medical Imaging Tasks via Vision-Language Models](https://mlsanity.com/paper/2503.15892/) (2025)
- [Neural Attention for Image Captioning: Review of Outstanding Methods](https://mlsanity.com/paper/2111.15015/) (2021)
- [DENEB: A Hallucination-Robust Automatic Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2409.19255/) (2024)
- [Diagnostic Captioning: A Survey](https://mlsanity.com/paper/2101.07299/) (2021)
- [MedMax: Mixed-Modal Instruction Tuning for Training Biomedical Assistants](https://mlsanity.com/paper/2412.12661/) (2024)
- [Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal Learning](https://mlsanity.com/paper/2505.17436/) (2025)
- [IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2210.15075/) (2022)
- [Structured Spectral Graph Representation Learning for Multi-label Abnormality Analysis from 3D CT Scans](https://mlsanity.com/paper/2510.10779/) (2025)
- [Medical visual question answering using joint self-supervised learning](https://mlsanity.com/paper/2302.13069/) (2023)
- [Contrastive Regularization over LoRA for Multimodal Biomedical Image Incremental Learning](https://mlsanity.com/paper/2508.11673/) (2025)
- [How to make Medical AI Systems safer? Simulating Vulnerabilities, and Threats in Multimodal Medical RAG System](https://mlsanity.com/paper/2508.17215/) (2025)
- [A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deception](https://mlsanity.com/paper/2208.05757/) (2022)
- [UIT-DarkCow team at ImageCLEFmedical Caption 2024: Diagnostic Captioning for Radiology Images Efficiency with Transformer Models](https://mlsanity.com/paper/2405.17002/) (2024)
- [Image-to-Text for Medical Reports Using Adaptive Co-Attention and Triple-LSTM Module](https://mlsanity.com/paper/2503.18297/) (2025)
- [Captioning Daily Activity Images in Early Childhood Education: Benchmark and Algorithm](https://mlsanity.com/paper/2604.01941/) (2026)

## Abstract (arXiv)

Image captioning applied to biomedical images can assist and accelerate the diagnosis process followed by clinicians. This article is the first survey of biomedical image captioning, discussing datasets, evaluation measures, and state of the art methods. Additionally, we suggest two baselines, a weak and a stronger one; the latter outperforms all current state of the art systems on one of the datasets.