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arxiv: 2605.25357 · v1 · pith:YCVTQDVRnew · submitted 2026-05-25 · 💻 cs.CV · cs.MA

Towards Reliable Fetal Ultrasound Interpretation with Multi-Agent Collaboration

Xiaotian Hu , Mingxuan Liu , Junwei Huang , Kasidit Anmahapong , Yifei Chen , Yiming Huang , Xuguang Bai , Zihan Li
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Hongjia Yang Yingqi Hao Hong Xu Yu Jiang Tian Tian Yi Liao Haibo Qu Qiyuan Tian
This is my paper

Pith reviewed 2026-06-29 22:49 UTC · model grok-4.3

classification 💻 cs.CV cs.MA
keywords fetal ultrasoundmulti-agent systemvisual question answeringmultimodal large language modelsmedical image analysisevidence arbitrationbenchmark datasetreport generation
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The pith

FetUSAgents uses multi-agent collaboration with visual tools and Dual-Path Evidence Arbitration to exceed MLLM baselines by more than 25 percent in fetal ultrasound VQA accuracy.

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

The paper establishes that single-model approaches limit integration across the steps of fetal ultrasound analysis from plane recognition to diagnostic reporting. FetUSAgents decomposes clinical queries into subtasks handled by coordinated LLM agents that invoke specialized visual tools, then applies Dual-Path Evidence Arbitration to blend deliberative reasoning with computational evidence. A retrieval-enhanced evidence bank stores intermediate findings for traceable conclusions. The system is evaluated on a new benchmark of 1892 images and 3205 question-answer pairs spanning ten tasks, with out-of-distribution tests showing gains over general and medical multimodal models. This setup targets reduced hallucination risk while supporting VQA, report generation, captioning, and video summarization.

Core claim

FetUSAgents is a tool-augmented multi-agent system that coordinates task-specific visual tools through collaborative LLM agents, decomposes queries from anatomical recognition to quantitative measurement, and applies Dual-Path Evidence Arbitration together with a retrieval-enhanced evidence bank to deliver traceable and clinically grounded outputs across visual question answering, report generation, image captioning, and video summarization.

What carries the argument

Dual-Path Evidence Arbitration (DPEA), which integrates LLM-based deliberative reasoning with structured computational evidence from specialized visual tools while a retrieval-enhanced evidence bank consolidates findings for traceable conclusions.

If this is right

  • FetUSAgents supports four tasks: visual question answering, report generation, image captioning, and video summarization.
  • The FetUS-VQA benchmark provides 1892 images and 3205 question-answer pairs across 10 clinical tasks for standardized evaluation.
  • Out-of-distribution experiments demonstrate more than 25 percent higher VQA accuracy than the strongest general or medical MLLM baseline.
  • The architecture offers a route toward evidence-driven clinical assistants for prenatal imaging.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same agent-coordination pattern could extend to other prenatal or obstetric imaging modalities that combine segmentation with measurement.
  • The evidence bank structure may support audit requirements if the system is deployed in regulated clinical environments.
  • Performance gains on VQA may translate to improved report consistency only if downstream clinical decisions are tracked in follow-up studies.
  • Real-time video summarization capability opens the possibility of live assistance during ultrasound exams rather than post-scan review.

Load-bearing premise

The Dual-Path Evidence Arbitration and retrieval-enhanced evidence bank produce clinically grounded conclusions without introducing new hallucination risks from the LLM agents.

What would settle it

A side-by-side comparison of FetUSAgents reports against expert fetal-medicine specialist interpretations on an independent clinical dataset, measuring diagnostic agreement rates and specific error categories.

Figures

Figures reproduced from arXiv: 2605.25357 by Haibo Qu, Hongjia Yang, Hong Xu, Junwei Huang, Kasidit Anmahapong, Mingxuan Liu, Qiyuan Tian, Tian Tian, Xiaotian Hu, Xuguang Bai, Yifei Chen, Yi Liao, Yiming Huang, Yingqi Hao, Yu Jiang, Zihan Li.

Figure 1
Figure 1. Figure 1: Overview of the FetUSAgents framework. The system routes user requests [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of datasets and tasks. (a) Representative in-distribution (ID) and [PITH_FULL_IMAGE:figures/full_fig_p014_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Semi-automated pipeline for FetUS-VQA construction. Given an image ID and [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: VQA accuracy on measurement tasks. Circles: group means; bars: ranges; stars: [PITH_FULL_IMAGE:figures/full_fig_p018_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: VQA accuracy on classification tasks. Circles: group means; bars: ranges; stars: [PITH_FULL_IMAGE:figures/full_fig_p018_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Representative examples of report generation across seven VQA tasks. The [PITH_FULL_IMAGE:figures/full_fig_p021_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Radar-chart comparison of report generation quality. (a, b) LLM-based and [PITH_FULL_IMAGE:figures/full_fig_p022_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Reference caption construction for LLM-based report evaluation. Task-specific [PITH_FULL_IMAGE:figures/full_fig_p023_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative examples of general-task outputs. (a) Image captioning: FetUSAgents [PITH_FULL_IMAGE:figures/full_fig_p024_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Radar-chart comparison of FetUSAgents (red curves and values) versus the [PITH_FULL_IMAGE:figures/full_fig_p025_10.png] view at source ↗
read the original abstract

Automated fetal ultrasound interpretation requires a workflow from visual perception, including plane recognition and anatomical segmentation, to clinical understanding, including biometric measurement and diagnostic reporting. However, the prevailing "one-task, one-model" paradigm limits systematic integration of evidence across this multi-step process. Although multimodal large language models (MLLMs) show promising visual understanding, their limited domain-specific grounding and hallucination risks restrict reliability in fetal ultrasound analysis. To address these limitations, we propose FetUSAgents, a tool-augmented multi-agent system for comprehensive fetal ultrasound interpretation, supporting visual question answering (VQA), report generation, image captioning, and video summarization. FetUSAgents coordinates task-specific visual tools through collaborative LLM agents and decomposes clinical queries into subtasks that progress from anatomical recognition to quantitative measurement. We further introduce Dual-Path Evidence Arbitration (DPEA), which integrates LLM-based deliberative reasoning with structured computational evidence from specialized visual tools. A retrieval-enhanced evidence bank consolidates intermediate findings to support traceable and clinically grounded conclusions. In addition, we construct FetUS-VQA, a dedicated VQA benchmark for fetal ultrasound, comprising 1,892 images and 3,205 question-answer pairs across 10 clinical tasks. Extensive out-of-distribution experiments show that FetUSAgents outperforms general and medical MLLMs, exceeding the strongest baseline by more than 25 percent in VQA accuracy. These results suggest a scalable route toward evidence-driven clinical assistants for prenatal imaging. Code is available.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces FetUSAgents, a tool-augmented multi-agent system for fetal ultrasound interpretation that coordinates task-specific visual tools (plane recognition, segmentation, biometry) via collaborative LLM agents, Dual-Path Evidence Arbitration (DPEA), and a retrieval-enhanced evidence bank. It constructs the FetUS-VQA benchmark (1,892 images, 3,205 QA pairs across 10 tasks) and claims that FetUSAgents outperforms general and medical MLLMs by more than 25% VQA accuracy on out-of-distribution experiments, with code released.

Significance. If the reported gains are shown to stem from the multi-agent architecture and DPEA rather than tool access alone, the approach could offer a scalable path to evidence-driven clinical assistants in prenatal imaging. The public release of code and the FetUS-VQA benchmark are clear strengths that support reproducibility and further research.

major comments (2)
  1. [Abstract] Abstract: The central claim that FetUSAgents exceeds the strongest baseline by more than 25% in VQA accuracy on OOD experiments does not specify whether the compared MLLM baselines were granted equivalent access to the same task-specific visual tools used by FetUSAgents. Without an ablation isolating the multi-agent layer (and DPEA) from tool augmentation, the performance delta cannot be attributed to the proposed architecture.
  2. [Abstract] Abstract: No details are supplied on baselines, statistical significance testing, error bars, dataset splits, or ablation studies supporting the >25% accuracy gain, preventing verification of the out-of-distribution performance claim from the provided text.
minor comments (1)
  1. [Abstract] The abstract states that FetUS-VQA covers '10 clinical tasks' but does not enumerate them; listing the tasks explicitly would improve clarity when describing the benchmark.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract. We address the two major comments below and will revise the manuscript to improve clarity on experimental details and attribution of gains.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that FetUSAgents exceeds the strongest baseline by more than 25% in VQA accuracy on OOD experiments does not specify whether the compared MLLM baselines were granted equivalent access to the same task-specific visual tools used by FetUSAgents. Without an ablation isolating the multi-agent layer (and DPEA) from tool augmentation, the performance delta cannot be attributed to the proposed architecture.

    Authors: We agree the abstract should explicitly distinguish the baselines. The compared MLLMs are standard general and medical models without access to the task-specific visual tools (plane recognition, segmentation, biometry) coordinated by the agents. The full manuscript contains ablation studies that isolate the multi-agent collaboration and DPEA from tool augmentation alone; we will revise the abstract to state this distinction and reference the relevant ablations. revision: yes

  2. Referee: [Abstract] Abstract: No details are supplied on baselines, statistical significance testing, error bars, dataset splits, or ablation studies supporting the >25% accuracy gain, preventing verification of the out-of-distribution performance claim from the provided text.

    Authors: The abstract is concise by design; the full manuscript specifies the MLLM baselines, describes the FetUS-VQA construction and splits, and presents ablation studies supporting the OOD gains. We will add a brief reference to the experimental protocol in the abstract. If statistical significance testing and error bars are not already reported in the results section, we will incorporate them in the revision. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical performance claims rest on external baselines

full rationale

The paper advances no mathematical derivation, first-principles equations, or fitted parameters that could reduce to self-defined inputs. Its central claim is an empirical accuracy delta (>25% VQA on OOD data) obtained by direct comparison to independent external MLLM baselines. No self-citation chains, ansatzes, or uniqueness theorems are invoked to justify the architecture; the system description and benchmark construction are presented as engineering contributions evaluated against outside references. This is the normal non-circular case for an applied systems paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 3 invented entities

The central claim rests on the effectiveness of the newly introduced multi-agent coordination, DPEA arbitration, and evidence bank; these components are defined within the paper and lack independent external validation in the abstract.

invented entities (3)
  • FetUSAgents no independent evidence
    purpose: Tool-augmented multi-agent system for fetal ultrasound interpretation
    Core proposed system; no independent evidence outside the paper
  • Dual-Path Evidence Arbitration (DPEA) no independent evidence
    purpose: Integrates LLM deliberative reasoning with structured computational evidence
    New arbitration method introduced by the authors
  • FetUS-VQA no independent evidence
    purpose: Dedicated VQA benchmark dataset
    New dataset constructed for this work

pith-pipeline@v0.9.1-grok · 5850 in / 1256 out tokens · 25266 ms · 2026-06-29T22:49:15.115950+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

6 extracted references · 6 canonical work pages · 4 internal anchors

  1. [1]

    Qwen3-VL Technical Report

    Qwen3-VL technical report. arXiv e-prints , arXiv:2511.21631doi:10. 48550/arXiv.2511.21631,arXiv:2511.21631. Bano, S., Vasconcelos, F., Amo-Aparicio, J., Teles Rodrigues, P., Curado, I., Dall’Asta, A., David, A.L., Deprest, J., Ourselin, S., Vercauteren, T., Melbourne, A., 2021. Autofb: Automating fetal biometry estimation from standard ultrasound planes,...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1109/tmi.2017 2021

  2. [2]

    DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models

    DeepSeek-V3.2: Pushing the frontier of open large language mod- els. arXiv e-prints , arXiv:2512.02556doi:10.48550/arXiv.2512.02556, arXiv:2512.02556. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.F., 2009. ImageNet: A large-scale hierarchical image database, in: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. doi:1...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2512.02556 2009

  3. [3]

    Optimal transport for machine learners.CoRR, abs/2505.06589, 2025

    nnu-net: a self-configuring method for deep learning-based biomed- ical image segmentation. Nature Methods 18, 203–211. doi:10.1038/ s41592-020-01008-z. 30 Jiang, S., Wang, Y., Song, S., Hu, T., Zhou, C.e.a., 2025. Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understanding. arXiv e-prints , arXiv:2510.08668doi: 10.4855...

    work page internal anchor Pith review doi:10.48550/arxiv 2025

  4. [4]

    Expert Systems with Applications238, 122153 (2024).https://doi.org/10.1016/j.eswa.2023.122153

    doi:10.1016/j.eswa.2023.122153. LangChain, 2026. Openaiembeddings. LangChain Python API Reference. Accessed: 2026-05-20. Li, C., Wong, C., Zhang, S., Usuyama, N., Liu, H., Yang, J., Naumann, T., Poon, H., Gao, J., 2023. LLaVA-med: Training a large language-and-vision assistant for biomedicine in one day, in: Thirty-seventh Conference on Neural Information...

    work page doi:10.1016/j.eswa.2023.122153 2023

  5. [5]

    Ultrasound in Obstetrics & Gynecology 37, 116–126

    Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan. Ultrasound in Obstetrics & Gynecology 37, 116–126. doi:10.1002/uog.8831. Sappia, M.S., de Korte, C.L., van Ginneken, B., Ninalga, D., Kondo, S., et al., 2025. Acouslic-ai challenge report: Fetal abdominal circumference measurement on blind-sweep ultrasound data from lo...

    work page doi:10.1002/uog.8831 2025

  6. [6]

    MedGemma Technical Report

    Medgemma technical report. arXiv e-prints , arXiv:2507.05201doi:10. 48550/arXiv.2507.05201,arXiv:2507.05201. Sendra-Balcells, C., Campello, V.M., Torrents-Barrena, J., et al., 2023. Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five african countries. Scientific Reports 13, 2728. doi:10.1038/s41598-023-29490...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1038/s41598-023-29490-3 2023