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arxiv: 2601.14180 · v4 · pith:UL72R6SKnew · submitted 2026-01-20 · 💻 cs.CV

Progressive mathcal{J}-Invariant Self-supervised Learning for Low-Dose CT Denoising

Yichao Liu , Zongru Shao , Yueyang Teng , Junwen Guo This is my paper

Pith reviewed 2026-05-25 06:55 UTC · model grok-4.3

classification 💻 cs.CV
keywords low-dose CTdenoisingself-supervised learningJ-invariantblind-spotMayo datasetGaussian noisePoisson noise
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0 comments X

The pith

Progressive J-invariant self-supervised learning achieves LDCT denoising comparable to supervised methods.

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

The paper introduces a progressive J-invariant learning method for denoising low-dose CT images using only noisy data. It employs a step-wise blind-spot mechanism to enforce conditional independence progressively for finer learning and injects controlled Gaussian and Poisson noise to regularize training. This addresses inefficiencies in existing self-supervised blind-spot methods with limited receptive fields. On the Mayo LDCT dataset, it outperforms other self-supervised approaches and matches or exceeds some supervised methods. A sympathetic reader would care because paired normal-dose and low-dose scans are hard to obtain in practice, limiting supervised training.

Core claim

By maximizing the use of J-invariance with a step-wise blind-spot denoising mechanism that enforces conditional independence progressively and by injecting a combination of controlled Gaussian and Poisson noise, the method produces a generalizable denoising function for LDCT that outperforms existing self-supervised approaches and achieves performance comparable to or better than several supervised methods on the Mayo dataset.

What carries the argument

The step-wise blind-spot denoising mechanism that enforces conditional independence in a progressive manner, enabling more fine-grained learning while using injected noise to regularize the process.

If this is right

  • Outperforms existing self-supervised denoising methods on the Mayo LDCT dataset.
  • Achieves performance comparable to or better than representative supervised denoising methods.
  • Mitigates training inefficiencies from restricted receptive fields in prior blind-spot approaches.
  • Reduces overfitting through explicit noise injection during training.

Where Pith is reading between the lines

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

  • If the progressive mechanism works across datasets, it could apply to other medical imaging tasks lacking paired data.
  • Combining this with different noise models might further improve robustness in varied clinical environments.
  • The approach suggests that progressive enforcement of independence properties can enhance self-supervised learning in image restoration generally.

Load-bearing premise

That the step-wise blind-spot mechanism combined with Gaussian and Poisson noise injection yields a denoising function generalizable beyond the Mayo dataset's specific noise characteristics.

What would settle it

Evaluating the trained model on an independent LDCT dataset from a different source and observing that it fails to match supervised method performance would indicate the method is tuned to the Mayo data rather than generally effective.

Figures

Figures reproduced from arXiv: 2601.14180 by Junwen Guo, Yichao Liu, YueYang Teng, Zongru Shao.

Figure 1
Figure 1. Figure 1: Overview of the progressive J -invariant learning. a) During training, a random mask is applied to the LDCT image at each time step. xˆ t Mi is the masked denoised image xMi at time t. b) During inference, multiple randomly masked versions of the same LDCT image are fed into the trained model, and the final denoised result is obtained by averaging the corresponding outputs. 3. Experiments We first introduc… view at source ↗
Figure 2
Figure 2. Figure 2: Results of pelvis image for comparison w.r.t. NDCT. (a)LDCT [PITH_FULL_IMAGE:figures/full_fig_p016_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Quantitative results on the test set of different patients. NBR2NBR denotes [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Quantitative results on the test set: number of time steps [PITH_FULL_IMAGE:figures/full_fig_p020_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Quantitative results on the test set: noise level [PITH_FULL_IMAGE:figures/full_fig_p021_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Quantitative results on the test set: Mask ratio for progressive [PITH_FULL_IMAGE:figures/full_fig_p022_6.png] view at source ↗
read the original abstract

Self-supervised learning has been increasingly investigated for low-dose computed tomography (LDCT) image denoising, as it alleviates the dependence on paired normal-dose CT (NDCT) data, which are often difficult to collect. However, many existing self-supervised blind-spot denoising methods suffer from training inefficiencies and suboptimal performance due to restricted receptive fields. To mitigate this issue, we propose a novel Progressive $\mathcal{J}$-invariant Learning that maximizes the use of $\mathcal{J}$-invariant to enhance LDCT denoising performance. We introduce a step-wise blind-spot denoising mechanism that enforces conditional independence in a progressive manner, enabling more fine-grained learning for denoising. Furthermore, we explicitly inject a combination of controlled Gaussian and Poisson noise during training to regularize the denoising process and mitigate overfitting. Extensive experiments on the Mayo LDCT dataset demonstrate that the proposed method consistently outperforms existing self-supervised approaches and achieves performance comparable to, or better than, several representative supervised denoising methods.

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 manuscript proposes Progressive J-Invariant Self-supervised Learning for low-dose CT (LDCT) denoising. It introduces a step-wise blind-spot mechanism that enforces conditional independence progressively to expand receptive fields beyond standard blind-spot methods, combined with explicit injection of controlled Gaussian and Poisson noise during training to regularize the process and reduce overfitting. The central claim, supported by experiments on the Mayo LDCT dataset, is that the approach consistently outperforms existing self-supervised denoising methods and achieves performance comparable to or better than representative supervised methods.

Significance. If the performance claims hold under broader validation, the work would advance self-supervised LDCT denoising by addressing receptive-field limitations in J-invariant methods through progressive enforcement and targeted noise regularization. This could meaningfully reduce dependence on paired normal-dose data. The ideas of step-wise blind-spot scheduling and dual-noise injection are technically interesting contributions to the self-supervised denoising literature.

major comments (2)
  1. [Experimental Results] Experimental Results section: All quantitative comparisons are confined to the Mayo LDCT dataset. This is load-bearing for the central claim of consistent outperformance versus self-supervised baselines and parity with supervised methods, because the controlled Gaussian+Poisson injection and blind-spot schedule are chosen with knowledge of Mayo noise statistics; without results on a second scanner, different dose-reduction factor, or non-Mayo protocol, it remains possible that the learned mapping exploits dataset-specific correlations rather than recovering a general J-invariant denoiser.
  2. [Method] Method section (description of progressive mechanism): The paper does not report an ablation isolating the contribution of the step-wise blind-spot schedule versus a fixed blind-spot baseline, nor does it quantify how the progressive schedule affects the conditional-independence property. Without these controls, it is unclear whether the reported gains derive from the claimed progressive J-invariance or from other implementation choices.
minor comments (2)
  1. [Abstract] The abstract states performance gains but supplies no numerical metrics, confidence intervals, or statistical tests; adding a concise quantitative summary would improve readability.
  2. [Method] Notation for the J-invariant property and the precise definition of the step-wise blind-spot mask should be introduced with an equation or diagram in the Method section to avoid ambiguity for readers unfamiliar with prior blind-spot work.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We respond point-by-point to the major concerns below.

read point-by-point responses

  1. Referee: [Experimental Results] Experimental Results section: All quantitative comparisons are confined to the Mayo LDCT dataset. This is load-bearing for the central claim of consistent outperformance versus self-supervised baselines and parity with supervised methods, because the controlled Gaussian+Poisson injection and blind-spot schedule are chosen with knowledge of Mayo noise statistics; without results on a second scanner, different dose-reduction factor, or non-Mayo protocol, it remains possible that the learned mapping exploits dataset-specific correlations rather than recovering a general J-invariant denoiser.

    Authors: We agree that results on additional datasets would strengthen claims of generalizability. The Mayo dataset remains the standard public benchmark for LDCT denoising and contains multiple dose levels and protocols. Our controlled noise injection follows general Gaussian-plus-Poisson models rather than Mayo-specific tuning. To address the concern, we will expand the experimental section with results on at least one additional LDCT dataset (or add an explicit limitations discussion if new acquisitions are not feasible within the revision timeline). revision: partial

  2. Referee: [Method] Method section (description of progressive mechanism): The paper does not report an ablation isolating the contribution of the step-wise blind-spot schedule versus a fixed blind-spot baseline, nor does it quantify how the progressive schedule affects the conditional-independence property. Without these controls, it is unclear whether the reported gains derive from the claimed progressive J-invariance or from other implementation choices.

    Authors: We acknowledge the value of an explicit ablation. In the revised manuscript we will add a controlled comparison of the progressive blind-spot schedule against a fixed blind-spot baseline, together with quantitative analysis of how the schedule influences the conditional-independence property. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain; claims rest on external dataset evaluation

full rationale

The paper describes a progressive J-invariant self-supervised method with step-wise blind-spot enforcement and controlled noise injection, then reports performance on the external Mayo LDCT dataset against baselines. No equations, self-definitions, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text that would reduce the claimed denoising function or outperformance to a tautology constructed from the method's own inputs. The central experimental claim uses an independent benchmark, satisfying the criteria for a self-contained result without circular reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides insufficient technical detail to enumerate specific free parameters, axioms, or invented entities; the method appears to rest on standard assumptions of blind-spot self-supervised learning plus the new progressive schedule.

pith-pipeline@v0.9.0 · 5697 in / 1062 out tokens · 28576 ms · 2026-05-25T06:55:37.615246+00:00 · methodology

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