Visualizing Uncertainty and Saliency Maps of Deep Convolutional Neural Networks for Medical Imaging Applications
Pith reviewed 2026-05-25 02:08 UTC · model grok-4.3
The pith
A pipeline for CNNs in medical imaging displays both prediction uncertainty and saliency maps of important pixels.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The paper claims that integrating uncertainty estimation with saliency mapping in one pipeline gives a more complete view of CNN decisions on medical images, showing both the model's confidence level and the specific pixels that most influence the output.
What carries the argument
The combined pipeline applying uncertainty quantification methods together with saliency techniques to the same CNN model outputs.
If this is right
- Clinicians receive visual indicators of both model reliability and the image features used.
- Model outputs can be inspected for cases where high uncertainty coincides with unexpected pixel focus.
- Debugging of CNN errors becomes possible by examining the highlighted regions.
- Potential for safer deployment of CNNs in medical workflows through added transparency.
Where Pith is reading between the lines
- The same pipeline structure might transfer to other image-based diagnostic tasks beyond the datasets tested.
- If the visualizations prove stable, they could serve as an input for human-in-the-loop review systems.
- Extending the pipeline to time-series medical data would require adapting the saliency component.
Load-bearing premise
Standard uncertainty estimation and saliency methods can be combined into a reliable pipeline without introducing new errors or requiring extra domain validation.
What would settle it
A medical imaging dataset where the uncertainty values and saliency maps are inconsistent with each other or with expert-labeled important regions would show the pipeline fails to deliver coherent information.
read the original abstract
Deep learning models are now used in many different industries, while in certain domains safety is not a critical issue in the medical field it is a huge concern. Not only, we want the models to generalize well but we also want to know the models confidence respect to its decision and which features matter the most. Our team aims to develop a full pipeline in which not only displays the uncertainty of the models decision but also, the saliency map to show which sets of pixels of the input image contribute most to the predictions.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript states the aim of developing a pipeline for deep convolutional neural networks in medical imaging that displays both the uncertainty of the model's decision and saliency maps to highlight contributing pixels in input images.
Significance. A validated pipeline combining uncertainty estimation and saliency mapping could improve safety and interpretability for clinical use of CNNs. The manuscript, however, contains no implementation, experiments, or validation, so no such contribution is demonstrated.
major comments (2)
- [Abstract] Abstract: The stated aim of a 'full pipeline' is unsupported by any methods, equations, datasets, results, or error analysis.
- [Abstract] Abstract: No evidence is provided that combining standard uncertainty methods (e.g., MC dropout) with saliency techniques (e.g., Grad-CAM) remains reliable in medical imaging without introducing new errors, such as inconsistent saliency under high uncertainty or poor calibration on expert-labeled data.
Simulated Author's Rebuttal
We thank the referee for the review and the opportunity to respond. We address the major comments point by point below.
read point-by-point responses
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Referee: [Abstract] Abstract: The stated aim of a 'full pipeline' is unsupported by any methods, equations, datasets, results, or error analysis.
Authors: We agree that the manuscript states the aim of developing a pipeline for displaying model uncertainty and saliency maps but does not include specific methods, equations, datasets, results, or error analysis. The provided text is a high-level description of the intended goal rather than a completed implementation. We will revise the abstract to clarify that the work describes a proposed direction and does not claim to present a validated pipeline. revision: yes
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Referee: [Abstract] Abstract: No evidence is provided that combining standard uncertainty methods (e.g., MC dropout) with saliency techniques (e.g., Grad-CAM) remains reliable in medical imaging without introducing new errors, such as inconsistent saliency under high uncertainty or poor calibration on expert-labeled data.
Authors: The referee is correct that the manuscript provides no empirical evidence, experiments, or analysis regarding the reliability of combining uncertainty estimation with saliency mapping, nor does it address potential issues such as inconsistent saliency or calibration. No such validation is present. We will revise the text to explicitly note that these aspects require future empirical investigation and are not demonstrated here. revision: yes
- Providing implementation details, datasets, experimental results, and validation of the proposed pipeline, as these are absent from the current manuscript.
Circularity Check
No circularity; pipeline combines standard off-the-shelf methods
full rationale
The manuscript describes an engineering pipeline that applies existing uncertainty estimation (e.g., MC dropout) and saliency techniques (e.g., Grad-CAM) to CNNs for medical images. No equations, fitted parameters presented as predictions, self-citation chains, uniqueness theorems, or ansatzes appear in the abstract or described content. The central claim is simply that the two families of methods can be displayed together; this does not reduce to any input by construction and contains no load-bearing self-referential steps.
discussion (0)
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