# Addressing Data Bias Problems for Chest X-ray Image Report Generation

arXiv: https://arxiv.org/abs/1908.02123 · HTML: https://mlsanity.com/paper/1908.02123/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2019.

## Built on by (most cited first, 14 of 14)

- [Competence-based Multimodal Curriculum Learning for Medical Report Generation](https://mlsanity.com/paper/2206.14579/) (2022)
- [Contrastive Attention for Automatic Chest X-ray Report Generation](https://mlsanity.com/paper/2106.06965/) (2021)
- [Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image Analysis](https://mlsanity.com/paper/2503.20047/) (2025)
- [Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation](https://mlsanity.com/paper/2109.12242/) (2021)
- [A Systematic Review of Deep Learning-based Research on Radiology Report Generation](https://mlsanity.com/paper/2311.14199/) (2023)
- [Token Imbalance Adaptation for Radiology Report Generation](https://mlsanity.com/paper/2304.09185/) (2023)
- [Rethinking Medical Report Generation: Disease Revealing Enhancement with Knowledge Graph](https://mlsanity.com/paper/2307.12526/) (2023)
- [On the Importance of Image Encoding in Automated Chest X-Ray Report Generation](https://mlsanity.com/paper/2211.13465/) (2022)
- [Textual Inversion and Self-supervised Refinement for Radiology Report Generation](https://mlsanity.com/paper/2405.20607/) (2024)
- ["Nothing Abnormal": Disambiguating Medical Reports via Contrastive Knowledge Infusion](https://mlsanity.com/paper/2305.08300/) (2023)
- [Rapid Review of Generative AI in Smart Medical Applications](https://mlsanity.com/paper/2406.06627/) (2024)
- [CT-FineBench: A Diagnostic Fidelity Benchmark for Fine-Grained Evaluation of CT Report Generation](https://mlsanity.com/paper/2604.24001/) (2026)
- [A Labeled Ophthalmic Ultrasound Dataset with Medical Report Generation Based on Cross-modal Deep Learning](https://mlsanity.com/paper/2407.18667/) (2024)
- [Med-R2: Perception and Reflection-driven Complex Reasoning for Medical Report Generation](https://mlsanity.com/paper/2504.02885/) (2025)

## Abstract (arXiv)

Automatic medical report generation from chest X-ray images is one possibility for assisting doctors to reduce their workload. However, the different patterns and data distribution of normal and abnormal cases can bias machine learning models. Previous attempts did not focus on isolating the generation of the abnormal and normal sentences in order to increase the variability of generated paragraphs. To address this, we propose to separate abnormal and normal sentence generation by using two different word LSTMs in a hierarchical LSTM model. We conduct an analysis on the distinctiveness of generated sentences compared to the BLEU score, which increases when less distinct reports are generated. We hope our findings will help to encourage the development of new metrics to better verify methods of automatic medical report generation.