Browse State-of-the-Art › Medical Report Generation
Medical Report Generation
46 papers with code · 3 benchmarks · 9 datasets archive 2025-07-28
Medical report generation (MRG) is a task which focus on training AI to automatically generate professional report according the input image data. This can help clinicians make faster and more accurate decision since the task itself is both time consuming and error prone even for experienced doctors.
Aggfgg
Deep neural network and transformer based architecture are currently the most popular methods for this certain task, however, when we try to transfer out pre-trained model into this certain domain, their performance always degrade.
The following are some of the reasons why RSG is hard for pre-trained models:
- Language datasets in a particular domain can sometimes be quite different from the large number of datasets available on the Internet
- During the fine-tuning phase, datasets in the medical field are often unevenly distributed
More recently, multi-modal learning and contrastive learning have shown some inspiring results in this field, but it's still challenging and requires further attention.
Here are some additional readings to go deeper on the task:
- On the Automatic Generation of Medical Imaging Reports
https://doi.org/10.48550/arXiv.1711.08195
- A scoping review of transfer learning research on medical image analysis using ImageNet
https://arxiv.org/abs/2004.13175
- A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis
https://arxiv.org/abs/2004.12150
(Image credit : Transformers in Medical Imaging: A Survey)
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
3 leaderboard tables shown for this task, 3 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| MIMIC-CXR (2 rows) | RGRG | Interactive and Explainable Region-guided Radiology Report Generation | code | Syntology ran 0 of 10 samples · 10 unverified | Compare |
| HistGen WSI-Report Dataset (1 row) | HistGen | HistGen: Histopathology Report Generation via Local-Global Feature... | code | Syntology ran 6 of 6 samples · 0 unverified | Compare |
| IU X-Ray (1 row) | X-RGen | Act Like a Radiologist: Radiology Report Generation across... | code | — | Compare |
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
9 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Most implemented papers archive 2025-07-28
30 shown of 46 papers with code (110 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
-
22 Nov 2017 4 repositories listedTo cope with these challenges, we (1) build a multi-task learning framework which jointly performs the pre- diction of tags and the generation of para- graphs, (2) propose a co-attention mechanism to localize regions…
-
30 Jun 2023 2 repositories listedWe present CausalVLR (Causal Visual-Linguistic Reasoning), an open-source toolbox containing a rich set of state-of-the-art causal relation discovery and causal inference methods for various visual-linguistic reasoning…
-
26 May 2023 2 repositories listedTo address these issues, we propose X-RGen, a radiologist-minded report generation framework across six anatomical regions.
-
16 Mar 2023 2 repositories listedMedical report generation (MRG) is essential for computer-aided diagnosis and medication guidance, which can relieve the heavy burden of radiologists by automatically generating the corresponding medical reports…
-
6 Jun 2020 2 repositories listedFirstly, the regions of primary interest to radiologists are usually located in a small area of the global image, meaning that the remainder parts of the image could be considered as irrelevant noise in the training…
-
23 Mar 2025 1 repository listedRetrieval-augmented generation (RAG) has emerged as a pivotal technique in artificial intelligence (AI), particularly in enhancing the capabilities of large language models (LLMs) by enabling access to external,…
-
20 Mar 2025 1 repository listedIn addition, it is applicable to multiple imaging modalities (e.
-
7 Mar 2025 1 repository listedHowever, existing evaluation metrics primarily assess the accuracy of key medical information coverage in generated reports compared to human-written reports, while overlooking crucial details such as the location and…
-
7 Jan 2025 1 repository listedX-ray image based medical report generation achieves significant progress in recent years with the help of the large language model, however, these models have not fully exploited the effective information in visual…
-
5 Dec 2024 1 repository listedIt significantly outperforms the state-of-the-art reference model by Qiu et al., achieving a METEOR score of 55.
-
19 Nov 2024 1 repository listedUnlike general vision-and-language models trained on diverse, non-specialized datasets, MVLMs are purpose-built for the medical domain, automatically extracting and interpreting critical information from medical images…
-
1 Oct 2024 1 repository listed Syntology ran 4 of 4 samples · 0 unverifiedThus, we conduct a comprehensive benchmarking of existing mainstream X-ray report generation models and large language models (LLMs), on the CheXpert Plus dataset.
-
19 Aug 2024 1 repository listedThey usually adopt a Transformer to extract the visual features of a given X-ray image, and then, feed them into the LLM for text generation.
-
16 Aug 2024 1 repository listed Syntology ran 5 of 14 samples · 9 unverified · 14 pointer-only (licence)We established a benchmark for the ECG report generation task and tested our model on multiple baselines.
-
4 Jul 2024 1 repository listed Syntology ran 5 of 5 samples · 0 unverifiedOur empirical assessments confirm MiniGPT-Med's superior performance in disease grounding, medical report generation, and VQA benchmarks, representing a significant step towards reducing the gap in assisting radiology…
-
2 Jul 2024 1 repository listedMulti-modal large language models (MLLMs) have been given free rein to explore exciting medical applications with a primary focus on radiology report generation.
-
23 May 2024 1 repository listedThis process allows the text decoder to attend to discriminative features of X-ray images, assimilate historical diagnostic information from similar cases, and understand the examination intention of patients.
-
15 May 2024 1 repository listedIn Stage 1, we introduce factuality-guided contrastive learning for visual representation by maximizing the semantic correspondence between radiographs and corresponding factual descriptions.
-
23 Apr 2024 1 repository listed Syntology ran 7 of 8 samples · 1 unverifiedSpecifically, we propose a cooperative framework, Generalist-Specialist Collaboration (GSCo), which consists of two stages, namely the construction of GFM and specialists, and collaborative inference on downstream tasks.
-
8 Mar 2024 1 repository listed Syntology ran 6 of 6 samples · 0 unverifiedHistopathology serves as the gold standard in cancer diagnosis, with clinical reports being vital in interpreting and understanding this process, guiding cancer treatment and patient care.
-
4 Mar 2024 1 repository listedOur paper reviews recent advancements in developing VLMs specialized for healthcare, focusing on models designed for medical report generation and visual question answering (VQA).
-
20 Feb 2024 1 repository listedPrevious research on radiology report generation has made significant progress in terms of increasing the clinical accuracy of generated reports.
-
5 Jan 2024 1 repository listedIn this paper, we propose a parameter efficient framework for fine-tuning MLLMs, specifically validated on medical visual question answering (Med-VQA) and medical report generation (MRG) tasks, using public benchmark…
-
7 Dec 2023 1 repository listedMedical report generation demands automatic creation of coherent and precise descriptions for medical images.
-
4 Nov 2023 1 repository listedThe goal of automatic report generation is to generate a clinically accurate and coherent phrase from a single given X-ray image, which could alleviate the workload of traditional radiology reporting.
-
21 Oct 2023 1 repository listedIt then combines the historical records, spatiotemporal information, and radiographs for report generation, where a disease progression graph and dynamic progression reasoning mechanism are devised to accurately select…
-
24 Aug 2023 1 repository listed Syntology ran 0 of 1 samples · 1 unverifiedTo address these challenges, we propose diagnosis-driven prompts for medical report generation (PromptMRG), a novel framework that aims to improve the diagnostic accuracy of MRG with the guidance of diagnosis-aware…
-
24 Jul 2023 1 repository listedKnowledge Graph (KG) plays a crucial role in Medical Report Generation (MRG) because it reveals the relations among diseases and thus can be utilized to guide the generation process.
-
10 Jun 2023 1 repository listedWith the availability of large-scale, comprehensive, and general-purpose vision-language (VL) datasets such as MSCOCO, vision-language pre-training (VLP) has become an active area of research and proven to be effective…
-
10 Jun 2023 1 repository listedThis paper explores the task of radiology report generation, which aims at generating free-text descriptions for a set of radiographs.
Syntology lines on 6 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections