# PDD-RRG: Posterior Diagnostic Decision for Study-level Radiology Report Generation

arXiv: https://arxiv.org/abs/2608.03055 · HTML: https://mlsanity.com/paper/2608.03055/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 30 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Builds on (most cited first, 22 of 22)

- [MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs](https://mlsanity.com/paper/1901.07042/) (2019)
- [Generating Radiology Reports via Memory-driven Transformer](https://mlsanity.com/paper/2010.16056/) (2020)
- [CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT](https://mlsanity.com/paper/2004.09167/) (2020)
- [On the Automatic Generation of Medical Imaging Reports](https://mlsanity.com/paper/1711.08195/) (2017)
- [Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation](https://mlsanity.com/paper/2106.06963/) (2021)
- [MAIRA-2: Grounded Radiology Report Generation](https://mlsanity.com/paper/2406.04449/) (2024)
- [Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation](https://mlsanity.com/paper/2010.10042/) (2020)
- [Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports](https://mlsanity.com/paper/2004.12274/) (2020)
- [ORGAN: Observation-Guided Radiology Report Generation via Tree Reasoning](https://mlsanity.com/paper/2306.06466/) (2023)
- [Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation](https://mlsanity.com/paper/2502.20056/) (2025)
- [HERGen: Elevating Radiology Report Generation with Longitudinal Data](https://mlsanity.com/paper/2407.15158/) (2024)
- [Longitudinal Data and a Semantic Similarity Reward for Chest X-Ray Report Generation](https://mlsanity.com/paper/2307.09758/) (2023)
- [Controllable Chest X-Ray Report Generation from Longitudinal Representations](https://mlsanity.com/paper/2310.05881/) (2023)
- [Radiology Report Generation via Multi-objective Preference Optimization](https://mlsanity.com/paper/2412.08901/) (2024)
- [Structural Entities Extraction and Patient Indications Incorporation for Chest X-ray Report Generation](https://mlsanity.com/paper/2405.14905/) (2024)
- [LLM-RG4: Flexible and Factual Radiology Report Generation across Diverse Input Contexts](https://mlsanity.com/paper/2412.12001/) (2024)
- [RADAR: Enhancing Radiology Report Generation with Supplementary Knowledge Injection](https://mlsanity.com/paper/2505.14318/) (2025)
- [Pragmatic Radiology Report Generation](https://mlsanity.com/paper/2311.17154/) (2023)
- [HC-LLM: Historical-Constrained Large Language Models for Radiology Report Generation](https://mlsanity.com/paper/2412.11070/) (2024)
- [The Impact of Auxiliary Patient Data on Automated Chest X-Ray Report Generation and How to Incorporate It](https://mlsanity.com/paper/2406.13181/) (2024)
- [A Comprehensive Graph Framework for Question Answering with Mode-Seeking Preference Alignment](https://mlsanity.com/paper/2506.17951/) (2025)
- [Enhanced Knowledge Injection for Radiology Report Generation](https://mlsanity.com/paper/2311.00399/) (2023)

## Abstract (arXiv)

Automatic radiology report generation (RRG) aims to simulate the workflow of radiologists, assisting them in clinical diagnosis. However, existing methods often fall short in utilizing all information relevant to the examination, as is typically done in clinical practice. Although some works attempt to incorporate multi-view images and historical data, these additional inputs may sometimes lead to avoidable diagnostic errors on the contrary. To address these challenges, we introduce a decision-making stage after report generation for the first time and propose a Posterior Diagnostic Decision framework (PDD-RRG) to integrate potentially conflicting diagnoses. Specifically, we create various subsets of input data and utilize an existing RRG model to generate reports from different perspectives. Then the Bayesian posterior probability and the learned thresholds for each clinical observation are calculated to obtain an aggregated diagnostic conclusion, which is subsequently used to refine the generated report. Experiments on MIMIC-CXR demonstrate that our proposed PDD-RRG can effectively enhance the clinical efficacy of existing RRG models without any retraining.