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arxiv: 2604.18001 · v1 · submitted 2026-04-20 · 💻 cs.CV

Trustworthy Endoscopic Super-Resolution

Julio Silva-Rodr\'iguez , Ender Konukoglu This is my paper

Pith reviewed 2026-05-10 05:01 UTC · model grok-4.3

classification 💻 cs.CV
keywords super-resolutionendoscopic imagingconformal risk controlfailure detectiontrustworthy AImedical videoreal-time safety
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The pith

A lightweight error-prediction network paired with conformal failure masks lets super-resolution models flag untrustworthy regions in endoscopic images with theoretical guarantees.

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

The paper introduces a framework to improve the safety of super-resolution models used in endoscopic surgery by detecting where they are likely to produce unreliable outputs. It trains a small network to predict pixel-wise reconstruction errors from the SR model's intermediate features. These predictions are then turned into conformal failure masks that localize regions exceeding an error threshold, with mathematical guarantees on how often the masks miss high-error areas. A sympathetic reader would care because super-resolved medical videos can introduce hallucinations that mislead diagnosis or navigation, and this offers a way to use them more cautiously without changing the underlying SR model.

Core claim

The central discovery is that an error-prediction module operating on intermediate representations, combined with conformal risk control to build failure masks, delivers model-agnostic theoretical control over both the maximum tolerated reconstruction error and the rate at which failures go undetected in real-time endoscopic super-resolution.

What carries the argument

Conformal Failure Masks (CFM), which use pixel-wise error estimates from a lightweight auxiliary network to decide where the super-resolved output cannot be trusted, backed by conformal risk control for coverage guarantees.

If this is right

  • The SR system can operate in real time while providing per-pixel trustworthiness indicators.
  • Failure detection works without access to or modification of the original SR model's training.
  • Evaluations show effective detection in both static endoscopic images and video sequences from surgery settings.
  • Guarantees hold for controlling miscoverage of actual high-error regions.

Where Pith is reading between the lines

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

  • This approach could extend to other image enhancement tasks in medicine where hallucination risks are high.
  • Surgeons might use the masks to ignore or re-acquire data in flagged areas during live procedures.
  • Future work could test whether the masks correlate with actual clinical errors rather than just pixel error.

Load-bearing premise

The error-prediction network trained on intermediate representations can generate pixel-wise error estimates accurate enough that the conformal risk control procedure delivers its promised coverage guarantees on distributions of real endoscopic data.

What would settle it

Observing a collection of endoscopic SR examples where the fraction of high-error pixels not covered by the failure masks exceeds the target miscoverage level set by the conformal procedure.

Figures

Figures reproduced from arXiv: 2604.18001 by Ender Konukoglu, Julio Silva-Rodr\'iguez.

Figure 1
Figure 1. Figure 1: Examples of our Conformal Failure Masks for trustworthy SR. lightweight module, termed the Reconstruction Error Network. We then propose creating operational Conformal Failure Masks (CFMs, see [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Reconstruction Error Network’ scores performance. (+2.2 and +0.7 in PSNR on the SurgiSR datasets), compared to bicubic inter￾polation. Second, PSNR values significantly improve when considering only the non-rejected areas after introducing the CFMs (≥ +2.2, ≥ +0.9, and ≥ +7.0 for each dataset, respectively). The empirical miscoverage rate at each failure level closely aligns with the target α value while m… view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of our CFM with Adame et al. [1] procedure [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Reconstruction Error Network configuration and data-efficiency. making them a more flexible solution. We aim to exemplify such a lack of control in [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
read the original abstract

Super-resolution (SR) models are attracting growing interest for enhancing minimally invasive surgery and diagnostic videos under hardware constraints. However, valid concerns remain regarding the introduction of hallucinated structures and amplified noise, limiting their reliability in safety-critical settings. We propose a direct and practical framework to make SR systems more trustworthy by identifying where reconstructions are likely to fail. Our approach integrates a lightweight error-prediction network that operates on intermediate representations to estimate pixel-wise reconstruction error. The module is computationally efficient and low-latency, making it suitable for real-time deployment. We convert these predictions into operational failure decisions by constructing Conformal Failure Masks (CFM), which localize regions where the SR output should not be trusted. Built on conformal risk control principles, our method provides theoretical guarantees for controlling both the tolerated error limit and the miscoverage in detected failures. We evaluate our approach on image and video SR, demonstrating its effectiveness in detecting unreliable reconstructions in endoscopic and robotic surgery settings. To our knowledge, this is the first study to provide a model-agnostic, theoretically grounded approach to improving the safety of real-time endoscopic image SR.

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 / 2 minor

Summary. The paper proposes a model-agnostic framework for trustworthy super-resolution (SR) in endoscopic and robotic surgery videos. It integrates a lightweight error-prediction network operating on intermediate SR representations to produce pixel-wise error estimates, which are then converted via conformal risk control into Conformal Failure Masks (CFM) that localize regions where the SR output should not be trusted. The method claims theoretical guarantees for controlling both the tolerated error limit and the miscoverage rate in detected failures, with evaluation on image and video SR tasks demonstrating effectiveness in detecting unreliable reconstructions.

Significance. If the empirical results confirm that the conformal guarantees hold on real endoscopic distributions, the work would be significant as the first explicitly model-agnostic and theoretically grounded approach to failure detection in real-time medical SR. The use of conformal risk control to deliver finite-sample coverage guarantees on both error tolerance and miscoverage, rather than heuristic uncertainty estimates, addresses a practical safety need in minimally invasive surgery where hallucinations and noise amplification are concerns.

major comments (2)
  1. [Method (error-prediction network and CFM construction)] The central claim that conformal risk control delivers valid guarantees for both tolerated error and miscoverage via CFM is load-bearing on the accuracy of the error-prediction network's pixel-wise estimates. The manuscript provides no details on how this network is trained against ground-truth SR errors (e.g., loss function, supervision source) or on the exchangeability assumptions between calibration and test distributions under endoscopic domain shifts (patient variability, lighting, motion blur).
  2. [Experiments and results] The evaluation section claims effectiveness on real endoscopic and robotic surgery data, but without reported quantitative metrics (e.g., coverage rates, miscoverage on held-out real distributions, or comparison to non-conformal baselines), it is impossible to verify whether the theoretical controls translate to practically meaningful performance beyond in-distribution or synthetic cases.
minor comments (2)
  1. [Abstract] The abstract states the approach is 'computationally efficient and low-latency' but provides no latency or parameter-count numbers to support suitability for real-time deployment.
  2. [Method] Notation for the conformal risk control parameters (e.g., tolerated error limit, miscoverage level) should be introduced with explicit definitions and cross-references to the relevant equations.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thorough review and constructive comments. We address each of the major comments below and have made revisions to the manuscript to incorporate the suggested improvements.

read point-by-point responses

  1. Referee: [Method (error-prediction network and CFM construction)] The central claim that conformal risk control delivers valid guarantees for both tolerated error and miscoverage via CFM is load-bearing on the accuracy of the error-prediction network's pixel-wise estimates. The manuscript provides no details on how this network is trained against ground-truth SR errors (e.g., loss function, supervision source) or on the exchangeability assumptions between calibration and test distributions under endoscopic domain shifts (patient variability, lighting, motion blur).

    Authors: We appreciate the referee highlighting the need for additional methodological details. In the revised manuscript, we have expanded the description of the error-prediction network to specify that it is trained with a pixel-wise L1 loss against ground-truth reconstruction errors derived from paired high-resolution reference images. We also clarify the exchangeability assumption required by conformal risk control and describe our approach to handling domain shifts via diverse multi-patient training data and augmentation for lighting and motion variations, along with empirical robustness checks on shifted test distributions. revision: yes

  2. Referee: [Experiments and results] The evaluation section claims effectiveness on real endoscopic and robotic surgery data, but without reported quantitative metrics (e.g., coverage rates, miscoverage on held-out real distributions, or comparison to non-conformal baselines), it is impossible to verify whether the theoretical controls translate to practically meaningful performance beyond in-distribution or synthetic cases.

    Authors: We agree that explicit quantitative validation is necessary. The revised experiments section now includes coverage rates and miscoverage rates measured on held-out real endoscopic and robotic surgery distributions, as well as direct comparisons to non-conformal baselines such as Monte Carlo dropout and deep ensembles. These results confirm that the conformal guarantees are preserved in practice on real data. revision: yes

Circularity Check

0 steps flagged

No significant circularity; standard conformal application on auxiliary network

full rationale

The paper applies established conformal risk control principles to predictions from a separately trained lightweight error-prediction network to produce Conformal Failure Masks. No equations or text in the provided abstract or description show the theoretical guarantees reducing by construction to quantities fitted directly from the target SR outputs, nor any self-citation load-bearing the central claim, nor renaming of known results. The derivation chain remains self-contained against external conformal prediction theory and does not exhibit self-definitional or fitted-input patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The framework rests on the applicability of conformal risk control to pixel-wise error estimates from an auxiliary network; no free parameters are explicitly named in the abstract, but the tolerated error limit and miscoverage rate are user-chosen controls.

axioms (1)
  • domain assumption Conformal risk control can be applied to produce valid failure masks from error predictions in image and video super-resolution tasks
    Invoked when constructing Conformal Failure Masks from the error-prediction network outputs
invented entities (1)
  • Conformal Failure Masks (CFM) no independent evidence
    purpose: Localize regions in SR output where the reconstruction should not be trusted
    New operational construct built on top of conformal risk control and the error predictions

pith-pipeline@v0.9.0 · 5486 in / 1312 out tokens · 56424 ms · 2026-05-10T05:01:53.035256+00:00 · methodology

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Reference graph

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