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arxiv: 2506.21834 · v2 · submitted 2025-06-27 · 💻 cs.CV

PrefPaint: Enhancing Medical Image Inpainting through Expert Human Feedback

Duy-Bao Bui , Hoang-Khang Nguyen , Thao Thi Phuong Dao , Kim Anh Phung , Tam V. Nguyen , Justin Zhan , Minh-Triet Tran , Trung-Nghia Le This is my paper

Pith reviewed 2026-05-19 08:27 UTC · model grok-4.3

classification 💻 cs.CV
keywords medical image inpaintingexpert human feedbackpolyp image synthesisstable diffusionhuman-in-the-loopclinical AIimage generationgastroenterology
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The pith

Expert feedback through a simple interface and efficient fine-tuning improves anatomical accuracy in generated polyp images.

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

The paper introduces PrefPaint as a practical system for medical image inpainting that brings in direct input from domain experts such as oncologists. It combines Stable Diffusion Inpainting with a lighter fine-tuning approach and a web interface that includes a Model Tree to track model versions during the feedback process. User studies indicate this produces polyp images with fewer visual errors and greater realism than prior methods. Such improvements matter because inaccurate synthetic images risk introducing misleading patterns that affect diagnostic AI tools in gastroenterology. A sympathetic reader sees value in making expert-guided image synthesis accessible for resource-limited clinical environments.

Core claim

PrefPaint is an interactive system that incorporates expert human feedback into Stable Diffusion Inpainting. By using D3PO for fine-tuning instead of full reward-model training and adding a web-based interface with Model Tree versioning, the method generates highly realistic and anatomically accurate polyp images. User studies confirm it outperforms existing inpainting techniques by reducing visual inconsistencies and producing outputs suitable for clinical AI applications.

What carries the argument

The Model Tree versioning interface that lets experts manage and iterate on fine-tuned inpainting models within a web platform, paired with D3PO to incorporate feedback efficiently.

If this is right

  • Generated polyp images show reduced visual inconsistencies compared with prior inpainting techniques.
  • The outputs achieve higher anatomical accuracy as judged by domain experts.
  • The system operates without computationally expensive reward models, suiting clinical settings with limited resources.
  • The resulting images support training and evaluation of clinical AI models more effectively.
  • The Model Tree interface simplifies expert participation in the fine-tuning loop.

Where Pith is reading between the lines

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

  • The same feedback-driven interface could extend to inpainting tasks in other medical specialties such as radiology or pathology.
  • Smaller clinics might use the approach to build custom synthetic datasets tailored to local patient populations.
  • Wider adoption could raise overall trust in synthetic medical data by tying image quality directly to specialist judgment.
  • The versioning concept might generalize to other interactive machine-learning tools that need to track incremental expert refinements.

Load-bearing premise

That collecting expert ratings through the proposed web interface and applying D3PO fine-tuning will produce reliable gains in anatomical accuracy and clinical utility without large-scale validation or full reward models.

What would settle it

A blinded rating study in which gastroenterologists score anatomical correctness of PrefPaint-generated polyp images against those from baseline inpainting methods, or a test of downstream polyp-detection model accuracy when trained on the new synthetic data versus conventional data.

Figures

Figures reproduced from arXiv: 2506.21834 by Duy-Bao Bui, Hoang-Khang Nguyen, Justin Zhan, Kim Anh Phung, Minh-Triet Tran, Tam V. Nguyen, Thao Thi Phuong Dao, Trung-Nghia Le.

Figure 1
Figure 1. Figure 1: Flow diagram illustrating the integration of human feedback in fine-tuning the inpainting models. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of our implemented D3PO [30]. The diffusion model generates two corresponding images based on the provided prompts. Guided by specific task requirements—such as improving prompt-image alignment or refining aesthetic quality—human evaluators select the preferred image. Leveraging this human feedback, our method directly updates the diffusion model’s parameters without necessitating the training of … view at source ↗
Figure 3
Figure 3. Figure 3: System architecture diagram. readability, making it easier to manage and update individual parts of the interface. • Integration: Templ seamlessly integrates with Golang, lever￾aging the language’s strengths in performance and devel￾opment ergonomics. This integration allows our website to be developed efficiently while ensuring that the overall ex￾perience is on par with other SSR frameworks in different … view at source ↗
Figure 4
Figure 4. Figure 4: Landing page and simple guide to web-based inter [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Overview of Model Tree and options when clicking [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 8
Figure 8. Figure 8: Task Manager for tracking current tasks. [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Information of participants. tasks are more time-consuming, often requiring up to 20 minutes to complete. To facilitate transparency and keep users informed about the status of their requests, the website includes a task manager feature, as illustrated in [PITH_FULL_IMAGE:figures/full_fig_p007_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Statistical ratings for each image inpainting method. Our proposed method outperforms all other methods in all [PITH_FULL_IMAGE:figures/full_fig_p008_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: User feedback on web-based interface usability [PITH_FULL_IMAGE:figures/full_fig_p009_11.png] view at source ↗
Figure 13
Figure 13. Figure 13: Qualitative comparison of outpainting results on [PITH_FULL_IMAGE:figures/full_fig_p010_13.png] view at source ↗
read the original abstract

Inpainting, the process of filling missing or corrupted image parts, has broad applications in medical imaging. However, generating anatomically accurate synthetic polyp images for clinical AI is a largely underexplored problem. In specialized fields like gastroenterology, inaccuracies in generated images can lead to false patterns and significant errors in downstream diagnosis. To ensure reliability, models require direct feedback from domain experts like oncologists. We propose PrefPaint, an interactive system that incorporates expert human feedback into Stable Diffusion Inpainting. By using D3PO instead of full RLHF, our approach bypasses the need for computationally expensive reward models, making it a highly practical choice for resource-constrained clinical settings. Furthermore, we introduce a streamlined web-based interface to facilitate this expert-in-the-loop training. Central to this platform is the Model Tree versioning interface, a novel HCI concept that visualizes the evolutionary progression of fine-tuned models. This interactive interface provides a smooth and intuitive user experience, making it easier to offer feedback and manage the fine-tuning process. User studies show that PrefPaint outperforms existing methods, reducing visual inconsistencies and generating highly realistic, anatomically accurate polyp images suitable for clinical AI applications.

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 manuscript proposes PrefPaint, an interactive system that incorporates expert human feedback into Stable Diffusion Inpainting via D3PO (instead of full RLHF) to generate anatomically accurate synthetic polyp images. It introduces a web-based interface featuring a novel Model Tree versioning concept for managing fine-tuned models, and claims via user studies that the approach reduces visual inconsistencies and produces images suitable for clinical AI applications in gastroenterology.

Significance. If the user-study claims are supported by quantitative metrics and downstream evaluations, the work could offer a practical, resource-efficient method for expert-in-the-loop fine-tuning of generative models in medical imaging, potentially improving synthetic data quality for clinical AI without the overhead of full RLHF pipelines. The Model Tree HCI element may also contribute to better interfaces for iterative model development.

major comments (2)
  1. [Abstract] Abstract: The central claim that 'User studies show that PrefPaint outperforms existing methods' and produces 'highly realistic, anatomically accurate polyp images suitable for clinical AI applications' is unsupported, as no metrics, baselines, statistical tests, or details on anatomical accuracy measurement are provided.
  2. [User studies / Experiments] User studies / Experiments section: No downstream task evaluations (e.g., polyp detection or segmentation performance using the generated images) are reported, leaving the leap from subjective preference data to clinical utility unsupported.
minor comments (1)
  1. [Abstract / Method] The expansion of the D3PO acronym and its precise relation to the fine-tuning objective should be stated explicitly on first use for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below, indicating revisions where appropriate to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that 'User studies show that PrefPaint outperforms existing methods' and produces 'highly realistic, anatomically accurate polyp images suitable for clinical AI applications' is unsupported, as no metrics, baselines, statistical tests, or details on anatomical accuracy measurement are provided.

    Authors: We agree that the abstract would be strengthened by including supporting details from the user studies. The experiments section reports results from a study with gastroenterologists using Likert-scale ratings for anatomical accuracy and visual fidelity, pairwise comparisons against baselines such as standard Stable Diffusion inpainting, and statistical significance via paired tests. We will revise the abstract to concisely reference these elements, for example by noting the expert preference rates and significance levels. revision: yes

  2. Referee: [User studies / Experiments] User studies / Experiments section: No downstream task evaluations (e.g., polyp detection or segmentation performance using the generated images) are reported, leaving the leap from subjective preference data to clinical utility unsupported.

    Authors: We acknowledge the value of downstream evaluations for demonstrating clinical utility. The current work centers on the expert-in-the-loop generation process and direct validation by gastroenterologists, who assessed images specifically for anatomical correctness and applicability to clinical AI training. This expert assessment provides a targeted measure of suitability rather than general preference. We have added a dedicated paragraph in the revised experiments section discussing potential benefits for downstream polyp detection based on expert qualitative feedback, while noting full quantitative downstream experiments as future work. revision: partial

Circularity Check

0 steps flagged

No circularity: claims rest on user studies and external benchmarks

full rationale

The paper introduces PrefPaint as an interactive system combining Stable Diffusion inpainting with D3PO fine-tuning and a web-based expert feedback interface. Central claims concern improved visual realism and anatomical accuracy for polyp images, validated via user studies. No equations, derivations, or first-principles predictions appear in the provided text. Performance assertions are tied to human evaluations rather than any fitted parameter renamed as a prediction or self-citation chain that reduces the result to its own inputs. The approach is self-contained against the reported user studies and does not invoke uniqueness theorems or ansatzes from prior self-work in a load-bearing way.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the approach inherits assumptions from Stable Diffusion and D3PO without stating new ones.

pith-pipeline@v0.9.0 · 5768 in / 987 out tokens · 27871 ms · 2026-05-19T08:27:59.806167+00:00 · methodology

discussion (0)

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