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A new Vietnamese PET/CT-report dataset improves medical VLM report generation and VQA, but clinical F1 scores remain modest.

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2026-08-04 13:51 UTC pith:DHRDSAFL

arxiv 2509.24739 v4 pith:DHRDSAFL submitted 2025-09-29 cs.CV

Toward a Vision-Language Foundation Model for Medical Data: Multimodal Dataset and Benchmarks for Vietnamese PET/CT Report Generation

Huu Tien Nguyen , Dac Thai Nguyen , The Minh Duc Nguyen , Trung Thanh Nguyen , Thao Nguyen Truong , Huy Hieu Pham , Johan Barthelemy , Minh Quan Tran
show 6 more authors
Thanh Tam Nguyen Quoc Viet Hung Nguyen Quynh Anh Chau Hong Son Mai Thanh Trung Nguyen Phi Le Nguyen
This is my paper
classification cs.CV
keywords medicalvlmsdatasetimagingclinicaldatavietnamesedevelopment
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

ViMed-PET collects PET/CT scans and full-length clinical reports from a Vietnamese hospital. The data is split into head-neck, chest, and abdomen-pelvis regions, creating 8,271 image-report pairs from 2,757 studies. The authors also build augmented datasets with GPT-4o: paraphrased reports, question-answer dialogues, and study comparisons. They then fine-tune four vision-language models by adapting a 3D vision encoder, aligning visual and text features, and instruction-tuning with LoRA.

On standard NLP metrics, the fine-tuned models improve dramatically over zero-shot baselines such as LLaVA-Med, M3D, RadFM, and few-shot GPT-4o. On a new clinical test set of 80 lung-cancer patients with 398 lesions, the models reach clinical F1 scores near 50% for the best configuration, well above GPT-4o's 24% but still far from clinically reliable performance.

The paper claims this is the first large-scale PET/CT dataset with paired Vietnamese clinical reports. The main weaknesses are that the dataset URL is not visible in the manuscript, clinical evaluation relies on GPT-4o to extract structured findings while GPT-4o also generated part of the training data, and no error bars are reported. The resource, if released, is useful for low-resource-language medical AI, but the experimental evidence for clinical utility is provisional.

Core claim

The paper's central claim is that ViMed-PET is the first large-scale paired dataset of PET/CT images and corresponding clinical reports in Vietnamese, and that fine-tuning VLMs on it leads to substantial performance improvements: 'fine-tuning VLMs on our proposed ViMed-PET dataset leads to substantial performance improvements across both standard NLP metrics and clinically specific evaluation metrics' (Section 4.1). If correct, the dataset is a genuinely new resource for PET/CT vision-language research and Vietnamese medical AI.

Load-bearing premise

The clinical evaluation pipeline assumes that GPT-4o-based structured extraction is an unbiased measure of clinical accuracy. GPT-4o generated the paraphrased reports, VQA dialogues, and study comparisons used for training, and the same model extracts the Type, Position, and FDG attributes from both generated and ground-truth reports (Sections 2.4, 3.4, B.4). If GPT-4o extraction favors texts that resemble its own generated style, the reported clinical F1 improvements may partly reflect style matching rather than clinical correctness. A second load-bearing premise is that the 20-slice overlap between adjacent body-part segments does not leak information between training and test splits; the paper does not specify a patient-exclusive split.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Axiom & Free-Parameter Ledger

4 free parameters · 5 axioms · 0 invented entities

No new physical or mathematical entities are introduced. The dataset and evaluation protocol are the contributions. The listed free parameters are discretionary choices in the evaluation and preprocessing pipeline rather than parameters of a theory; they affect the reported numbers but are not fitted to a target result. The axioms are background assumptions about data provenance, modality semantics, and the reliability of GPT-4o-based attribute extraction.

free parameters (4)
  • Body-part split ratios = head-neck ~20% of body length; chest starts ~15% below the last head-neck slice; abdomen-pelvis is the remainder
    Expert-defined proportions that determine which slices and report sections are paired; directly affects the 8,271 paired samples and the 20-slice overlap.
  • Reconstruction loss weighting lambda = 1e-2
    Weights the L1 and inverted SSIM terms in Eq. (1) for Cosmos Tokenizer fine-tuning; not derived from data.
  • Clinical attribute categories and grouping rules = Type: 12 classes; FDG: 2 classes; Position: 5 classes; plus semantic grouping rules
    Expert-chosen taxonomy used to define F1-T, F1-TP, F1-TF, F1-TPF; different taxonomies or grouping rules would change the reported clinical scores.
  • Fixed slice depth for Cosmos Tokenizer = 120 slices
    Chosen from the dataset distribution; zero-padding or linear interpolation to 120 slices can add or lose anatomical content.
axioms (5)
  • domain assumption Report text is driven primarily by PET information, with minimal reference to CT anatomical details.
    Stated in Section 5; motivates treating PET/CT volumes as the visual input and ignoring CT-specific content. If false, the dataset and benchmark would be misaligned with the reports.
  • domain assumption Informed consent and de-identification were properly obtained and satisfy privacy regulations (Ethics Approval No. 6184/CN-HDDDBV).
    Section 2.2; needed for legal and ethical distribution of a hospital-derived dataset.
  • domain assumption CT-ViT, pretrained on non-contrast chest CT volumes, transfers to whole-body PET/CT after contrastive fine-tuning.
    Section 3.2 Stage 1; stage-1 fine-tuning relies on transfer from a different modality and anatomy to the target PET/CT data.
  • domain assumption GPT-4o few-shot extraction of clinical attributes is reliable after verification by two physicians.
    Appendix B.4; the entire clinical F1 pipeline assumes structured extraction matches physician judgment for both reference and generated reports, including handling of 'other' categories.
  • domain assumption The 2,757 studies come from independent patients.
    Section 2.2 claims independent patients; if patients have repeated scans, the independence claim and the split could be violated.

pith-pipeline@v1.3.0-alltime-deepseek · 28811 in / 10503 out tokens · 85569 ms · 2026-08-04T13:51:39.559206+00:00 · methodology

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Cite this review

Pith. "Pith review of Toward a Vision-Language Foundation Model for Medical Data: Multimodal Dataset and Benchmarks for Vietnamese PET/CT Report Generation." pith.science (2026). https://pith.science/paper/DHRDSAFL

@misc{pith2026250924739,
  author       = {Pith},
  title        = {Pith review of: Toward a Vision-Language Foundation Model for Medical Data: Multimodal Dataset and Benchmarks for Vietnamese PET/CT Report Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DHRDSAFL}},
  note         = {Machine review of arXiv:2509.24739}
}
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read the original abstract

Vision-Language Foundation Models (VLMs), trained on large-scale multimodal datasets, have driven significant advances in Artificial Intelligence (AI) by enabling rich cross-modal reasoning. Despite their success in general domains, applying these models to medical imaging remains challenging due to the limited availability of diverse imaging modalities and multilingual clinical data. Most existing medical VLMs are trained on a subset of imaging modalities and focus primarily on high-resource languages, thus limiting their generalizability and clinical utility. To address these limitations, we introduce a novel Vietnamese-language multimodal medical dataset consisting of 2,757 whole-body PET/CT volumes from independent patients and their corresponding full-length clinical reports. This dataset is designed to fill two pressing gaps in medical AI development: (1) the lack of PET/CT imaging data in existing VLMs training corpora, which hinders the development of models capable of handling functional imaging tasks; and (2) the underrepresentation of low-resource languages, particularly the Vietnamese language, in medical vision-language research. To the best of our knowledge, this is the first dataset to provide comprehensive PET/CT-report pairs in Vietnamese. We further introduce a training framework to enhance VLMs' learning, including data augmentation and expert-validated test sets. We conduct comprehensive experiments benchmarking state-of-the-art VLMs on downstream tasks. The experimental results show that incorporating our dataset significantly improves the performance of existing VLMs. We believe this dataset and benchmark will serve as a pivotal step in advancing the development of more robust VLMs for medical imaging, especially for low-resource languages and clinical use in Vietnamese healthcare. The source code is available at https://github.com/AIoT-Lab-BKAI/ViPET-ReportGen.

Figures

Figures reproduced from arXiv: 2509.24739 by Dac Thai Nguyen, Hong Son Mai, Huu Tien Nguyen, Huy Hieu Pham, Johan Barthelemy, Minh Quan Tran, Phi Le Nguyen, Quoc Viet Hung Nguyen, Quynh Anh Chau, Thanh Tam Nguyen, Thanh Trung Nguyen, Thao Nguyen Truong, The Minh Duc Nguyen, Trung Thanh Nguyen.

Figure 1
Figure 1. Figure 1: An example from our ViMed-PET dataset. The visual input consists of aligned 3D CT [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the fine-tuning pipeline. Stage 1: Fine-tuning the 3D Vision Encoder with [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Message used to prompt GPT-4o to generate our medical VQA conversations. Manually [PITH_FULL_IMAGE:figures/full_fig_p022_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Few-shot examples included in our prompt to construct the VQA conversation dataset. [PITH_FULL_IMAGE:figures/full_fig_p023_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: System message used to prompt GPT-4o for generating the study comparison dataset. The [PITH_FULL_IMAGE:figures/full_fig_p023_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Few-shot example used in our prompt for generating the study comparison dataset. The [PITH_FULL_IMAGE:figures/full_fig_p024_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Clinical evaluation pipeline. Phase 1: Experts define structured clinical attributes from [PITH_FULL_IMAGE:figures/full_fig_p026_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Message used to prompt GPT-4o for structuring VLM-generated reports into JSON format. [PITH_FULL_IMAGE:figures/full_fig_p027_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Example of a few-shot prompt used to guide GPT-4o in extracting structured JSON data [PITH_FULL_IMAGE:figures/full_fig_p027_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Visualization of the flattening strategy. Consecutive 2D slices from a 3D medical volume [PITH_FULL_IMAGE:figures/full_fig_p028_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Prompt template used with GPT-4o to analyze concatenated 2D grid images and generate [PITH_FULL_IMAGE:figures/full_fig_p028_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Ground truth and generated PET/CT reports for the chest and abdomen-pelvis regions [PITH_FULL_IMAGE:figures/full_fig_p030_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Short-form VQA interaction in Vietnamese (EN: translated) between a user and the [PITH_FULL_IMAGE:figures/full_fig_p031_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Long-form VQA interaction in Vietnamese (EN: translated) using the CT-ViT + Mistral [PITH_FULL_IMAGE:figures/full_fig_p032_14.png] view at source ↗

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Forward citations

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