Relevance analysis of MRI sequences for automatic liver tumor segmentation
Pith reviewed 2026-05-24 14:58 UTC · model grok-4.3
The pith
Layer-wise relevance propagation attributes segmentation decisions back to individual MRI input sequences for liver tumor models.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The central claim is that layer-wise relevance propagation can be extended from classification to semantic segmentation networks and used to compute per-sequence relevance scores that indicate which MRI sequences drive the automatic liver tumor segmentation output.
What carries the argument
Layer-wise relevance propagation applied channel-wise to the multi-sequence MRI input of a segmentation network to produce relevance maps and aggregate scores per sequence.
If this is right
- MRI sequences can be ranked by their contribution to liver tumor segmentation accuracy.
- Low-relevance sequences may be omitted from future scans or model inputs with limited impact on performance.
- Model validation becomes possible by checking whether high-relevance sequences align with radiological expectations.
- The same relevance scores can guide targeted improvements to the network architecture or training data.
Where Pith is reading between the lines
- The method could be applied to other multi-modal medical segmentation tasks to optimize the number of required imaging sequences.
- Clinical workflows might use the rankings to shorten MRI protocols while preserving diagnostic utility for tumor segmentation.
- If relevance attributions prove stable across different network architectures, they could serve as a general tool for comparing input modalities.
Load-bearing premise
Layer-wise relevance propagation produces faithful and unbiased attributions from the segmentation output to the individual MRI input sequences.
What would settle it
An ablation study in which sequences ranked highest by relevance propagation are removed and the segmentation Dice score drops no more than when low-ranked sequences are removed would falsify the attribution faithfulness.
read the original abstract
Explainability of decisions made by deep neural networks is of high value as it allows for validation and improvement of models. This work proposes an approach to explain semantic segmentation networks by means of layer-wise relevance propagation. As an exemplary application, we investigate which MRI sequences are most relevant for liver tumor segmentation.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript proposes an approach to explain semantic segmentation networks by means of layer-wise relevance propagation (LRP). As an exemplary application, it investigates which MRI sequences are most relevant for liver tumor segmentation.
Significance. If LRP attributions prove faithful in this multi-sequence setting, the work could contribute to interpretability of medical segmentation models and inform sequence selection. However, the absence of any quantitative checks on attribution quality limits the potential impact, as the central claim rests on unverified faithfulness of the relevance scores.
major comments (1)
- The central claim requires that LRP attributions from the segmentation output back to the four input MRI sequences are faithful. The manuscript applies standard LRP rules to a U-Net-like architecture but provides no ablation, perturbation, or comparison against other attribution methods to test whether the resulting per-sequence relevance scores are unbiased or predictive of actual model behavior. This is load-bearing for the exemplary application.
Simulated Author's Rebuttal
We thank the referee for their review. We address the major comment regarding validation of LRP attributions below.
read point-by-point responses
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Referee: The central claim requires that LRP attributions from the segmentation output back to the four input MRI sequences are faithful. The manuscript applies standard LRP rules to a U-Net-like architecture but provides no ablation, perturbation, or comparison against other attribution methods to test whether the resulting per-sequence relevance scores are unbiased or predictive of actual model behavior. This is load-bearing for the exemplary application.
Authors: We recognize the referee's point but note that the manuscript presents LRP as a tool for an exemplary analysis of sequence relevance rather than a validation study of attribution methods. Standard LRP rules are applied to a U-Net-like model following established implementations; the work does not assert that the resulting scores have been quantitatively verified as faithful or unbiased. The central contribution is the demonstration of the approach on multi-sequence MRI data and the resulting per-sequence relevance insights. We maintain that additional ablations or comparisons fall outside the stated scope. revision: no
Circularity Check
No circularity: standard LRP application to segmentation network
full rationale
The paper applies layer-wise relevance propagation (a pre-existing method) to attribute relevance from a U-Net segmentation output back to input MRI sequences. No equations or derivations are presented that reduce by construction to fitted inputs, self-definitions, or self-citation chains. The central claim (which sequences are most relevant) is an empirical output of the attribution process rather than a tautological renaming or forced prediction. The method is self-contained against external LRP literature and does not rely on load-bearing self-citations for its validity.
discussion (0)
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