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REVIEW 4 major objections 6 minor 36 references

MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read MedGrad E-CLIP produces visual explanations for CLIP-based skin lesion classification that capture fine-grained diagnostic details that Grad E-CLIP and Grad-CAM miss.

desk verdict Plausible entropy-weighting extension to Grad E-CLIP, but the 'superior' claim rests on two eyeballed heatmaps and an underspecified formula. read the letter →

arxiv 2501.06887 v1 pith:D5XZBSHP submitted 2025-01-12 cs.CV cs.AIcs.ETcs.LG

Sadia Kamal , Tim Oates This is my paper · ORCID
classification cs.CVcs.AIcs.ETcs.LG
keywords skinlesionclassificationCLIPexplainabilityentropyweightinggradient-basedexplanationsdermoscopyvision-languagemodelsmedicalimageanalysis
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper proposes MedGrad E-CLIP, an explainability method that replaces the spatial importance weights in gradient-based E-CLIP with weights derived from local pixel-intensity entropy, aiming to make CLIP-based skin lesion classification more transparent for clinicians. The authors train a CLIP model on dermoscopic images paired with textual diagnostic criteria from the PH2 and Derm7pt datasets, then use entropy-weighted gradients to highlight image regions aligned with specific descriptions. They claim the resulting heatmaps capture subtle, diagnostically significant features—such as light brown areas and symmetry in one axis—that Grad E-CLIP and Grad-CAM overlook. If correct, the method would give dermatologists a way to see which visual evidence drove an AI diagnosis, supporting trust and verification in AI-assisted skin cancer screening.

What carries the argument

The key mechanism is the entropy-weighted aggregation in Eq. (3): $H_i = \operatorname{ReLU}\left(\sum_c w_c v_i w_e\right)$, where $w_c$ is channel importance, $v_i$ is the pixel value at spatial location $i$, and $w_e$ is a weight derived from the local entropy of pixel intensities in a disk around each pixel. This replaces Grad E-CLIP's spatial importance term, so the explanation is driven by regions with high local complexity and information density rather than by the most gradient-activated regions. The paper also relies on the CLIP contrastive pipeline, which scores an image-text pair by cosine similarity between image and text embeddings, trained on PH2 and Derm7pt descriptions to align lesion images with diagnostic criteria.

What would settle it

A quantitative study in which MedGrad E-CLIP, Grad E-CLIP, and Grad-CAM heatmaps are scored against expert-annotated dermoscopic structures such as pigment network, streaks, or regression areas on PH2 or Derm7pt, using insertion/deletion or pointing-game metrics; if entropy-weighted heatmaps do not outperform the baselines on these metrics, the claimed superiority would be refuted.

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Extended reading notes

Core claim

The central claim is that MedGrad E-CLIP provides superior visual explanations for CLIP-based skin lesion diagnosis by emphasizing fine-grained details. The paper argues that its weighted entropy mechanism, computed as local intensity entropy within a disk around each pixel and applied multiplicatively to gradient contributions, shifts attention toward regions with high pixel-level complexity and information density rather than merely the most activated areas. In qualitative comparisons on Atypical Nevus and Melanoma images, the method highlights features such as a slight light brown area outside the lesion and symmetry in one axis that the original Grad E-CLIP misses, while also aligning with textual descriptions like 'fully symmetric' and 'light brown color.' The same trained CLIP model improves test accuracy from 2.06% before training to 80.08% after training on the custom skin-lesion dataset, which the authors take as evidence that the model has learned meaningful image-text relationships that the explanations then expose.

Load-bearing premise

The paper assumes that local pixel-intensity entropy is a good proxy for diagnostically significant features, so that weighting gradients by entropy highlights clinically meaningful regions; it does not validate this assumption against clinical ground truth or quantitative explanation metrics.

Editorial extensions

If this is right

  • Clinicians could see which part of a lesion drove a CLIP classification, with heatmaps tied to specific textual diagnostic criteria such as symmetry, pigmentation, and streaks.
  • The method addresses known weaknesses of Grad E-CLIP, which struggles with physical and comparative attributes, by capturing features like full asymmetry and symmetry in one axis.
  • Training CLIP on skin-lesion image-text pairs lifts test accuracy from 2.06% to 80.08%, so the explainability gains come alongside a functional classifier.
  • The pipeline combines classification and explanation in one pass, so no separate post-hoc model is needed to produce visual justifications.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A quantitative evaluation using insertion/deletion metrics or a pointing game against expert-annotated dermoscopic structures would test whether entropy weighting truly tracks diagnostic relevance; the paper itself lists this as future work.
  • Because entropy weighting responds to pixel-level variation, it could also amplify noise or texture artifacts in low-quality dermoscopic images, a risk the paper acknowledges as over-explanation in some cases.
  • The same entropy-weighting design could transfer to other medical imaging domains where text descriptions are paired with images, such as radiology reports, not just skin lesions.
  • The sizable accuracy jump from 2.06% to 80.08% likely reflects the model learning dataset-specific text-image associations rather than general dermatologic knowledge; calibration on external datasets would clarify this.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes MedGrad E-CLIP, a modification of the gradient-based explainability method Grad E-CLIP for CLIP-based skin lesion classification. The method replaces Grad E-CLIP's spatial importance with a locally computed entropy weight intended to highlight subtle, diagnostically relevant regions. The authors fine-tune CLIP on PH² and Derm7pt images paired with textual descriptions, report classification accuracy before and after fine-tuning, and qualitatively compare heatmaps produced by Grad-CAM, Grad E-CLIP, and MedGrad E-CLIP on two example cases (atypical nevus and melanoma). The paper concludes that MedGrad E-CLIP provides superior visual explanations and better alignment with textual descriptions.

Significance. If fully specified and rigorously validated, an entropy-weighted gradient explanation method for CLIP could be a useful contribution to interpretable medical image analysis, particularly for clinician-facing tools that need to connect visual evidence to diagnostic criteria. The paper addresses a real gap: gradient-based CLIP explanations are known to miss subtle features in medical images, and the idea of using local entropy to redistribute attention is plausible. The authors also make a concrete attempt to apply CLIP to two public dermoscopic datasets and compare explanation methods on the same model. However, the contribution as presented is not yet assessable: the method is incompletely specified, and the central claim of superiority is supported only by visual inspection of two images, with the paper itself deferring quantitative validation to future work. The manuscript in its current form does not meet the evidentiary standard needed to support the claimed level of trust and transparency.

major comments (4)
  1. [§3.4, Eq. (3)] The entropy weight w_e is never defined. Equation (3) defines H_i = ReLU(Σ_c w_c v_i w_e), but the manuscript does not specify how w_e is computed from local entropy, the disk radius used in the neighborhood, the number of gray-level bins, or the normalization procedure. Section 4 only says local entropy is computed 'within the region covered by the disk around each pixel,' which is insufficient for reproduction. Without a complete definition, the reader cannot determine what MedGrad E-CLIP actually computes, and the displayed heatmaps cannot be attributed to a reproducible algorithm.
  2. [§5.2, Figures 5 and 6] The central claim that MedGrad E-CLIP 'provides superior visual explanations' rests entirely on the visual inspection of two example images, one atypical nevus and one melanoma. No quantitative explanation metrics (such as insertion/deletion scores, pointing game, IoU against dermoscopic annotations, or a clinician user study) are reported, and Section 6 explicitly defers such assessments to future work. The comparison with Grad-CAM and Grad E-CLIP is therefore anecdotal; it does not provide evidence for the strong comparative conclusion stated in the Conclusion.
  3. [§3.4] The method rests on an unvalidated assumption that higher local pixel-intensity entropy marks diagnostically significant regions. This assumption can fail in dermoscopic images: hair, scale, compression artifacts, and lesion borders often have high local entropy without being clinically meaningful. The paper provides no test of this assumption against dermoscopic structure annotations or any external ground truth, so the heatmaps may highlight irrelevant texture or noise rather than the diagnostic features the method claims to capture.
  4. [§5.1, Table 2] The classification results are reported as aggregate accuracy, F1, and loss on a single train/test split, with no comparison to standard skin-lesion classifiers, no per-class breakdown, no confidence intervals, and no explanation for the pre-training accuracy of 2.06% on a 17-class task. While classification performance is not the paper's main focus, the claim that the pipeline 'not only classifies... but also adds explainability' is not supported by a systematic evaluation, and the unusually low pre-fine-tuning accuracy suggests a possible label or prompting issue that should be clarified.
minor comments (6)
  1. [§3.2] The section heading contains a typo ('Prepration' should be 'Preparation'), and there are several grammatical errors such as 'These includes' and 'images are resized to 224x224 pixels to meet the input requirements of the image encoder' — the latter is acceptable but the sentence flow should be revised.
  2. [§5.2, Figures 5 and 6] The figure captions do not state which text prompt or class label was used to generate each row of heatmaps. Since the paper claims alignment with textual descriptions, each panel should specify the exact text input, and the captions should be self-contained.
  3. [§3.3, Eq. (4)] Equation (4) introduces fimg and ftext without defining them in terms of the earlier notation fI and fT. Use consistent notation for image and text features throughout.
  4. [References] Several references are incomplete or inconsistent: reference [12] lists 'Seven-Point Checklist' as an author, and reference [15] cites a paper titled 'Deep-Lift' that is not the original DeepLIFT paper. Please correct the bibliography.
  5. [§3.1 and §4] The paper states that the dataset comprises 17 distinct skin lesion types, but the description of PH² and Derm7pt does not explain how 17 classes are constructed from these two datasets or how overlapping cases are handled. Clarify the class definitions and the source of the text descriptions used for each class.
  6. [§6] The paragraph thanking referees is unusual in a submitted manuscript and should be removed; it also has no place in the final version before the acknowledgment section.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: MedGrad E-CLIP's entropy weighting is computed from image statistics rather than fitted to explanation outputs, and the paper's central claim rests on qualitative comparison, not on a self-referential derivation.

full rationale

The paper's proposed method modifies Grad E-CLIP by multiplying gradient-based spatial importance with an entropy weight (Eq. 3). The entropy weights are described as being computed from local pixel-intensity distributions, not fitted to the classification labels or to the explanation heatmaps, so the 'superior visual explanations' claim is not forced by construction. The CLIP classifier is trained with a standard contrastive image-text loss and evaluated on a held-out test split; no fitted parameter is renamed as a prediction. Prior work is used only as external baselines and dataset references: Grad-CAM, Grad E-CLIP [36], and the PH2/Derm7pt datasets. There are no load-bearing self-citations, and no uniqueness theorem or ansatz is imported from the authors' own prior work. The paper does contain a genuine evidence gap: Section 6 defers quantitative assessment such as insertion and deletion analysis and statistical significance tests to future work, and the entropy weight formula is incompletely specified in Section 4. However, missing validation and under-specification are correctness or reproducibility concerns, not circular reasoning. The derivation chain does not reduce to its inputs by definition or by construction, so the appropriate circularity score is 0.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new physical or conceptual entities. Its central method rests on a few assumptions and an unspecified entropy weighting procedure, which are the main sources of uncertainty.

free parameters (2)
  • Local entropy neighborhood size (disk radius) = Not specified
    The entropy weights in Eq. (3) depend on computing local entropy within a disk around each pixel, but the disk radius is never reported. The results depend on this unspecified parameter.
  • Channel importance weights (wc) and entropy weights (we) = Not explicitly defined
    Eq. (3) introduces wc and we but their computation is not formalized beyond a generic description; these act as ad hoc weights controlling the heatmap.
assumptions (3)
  • domain assumption CLIP's pretrained representations transfer to dermoscopic images
    The method relies on CLIP to encode skin lesion images and text; the paper fine-tunes it but assumes the base model contains useful visual features.
  • ad hoc to paper Entropy of pixel intensities indicates diagnostic importance
    Section 3.4 assumes higher local entropy corresponds to subtle, diagnostically significant features. This is not derived or validated.
  • domain assumption The image-text descriptions in PH2 and Derm7pt are accurate and aligned
    Training and explanation rely on paired descriptions; if descriptions are noisy or incomplete, the learned alignments and explanations would be incorrect.

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Cite this review

Pith. "Pith review of MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis." pith.science (2026). https://pith.science/paper/D5XZBSHP

@misc{pith2026250106887,
  author       = {Pith},
  title        = {Pith review of: MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D5XZBSHP}},
  note         = {Machine review of arXiv:2501.06887}
}
read the original abstract

As deep learning models gain attraction in medical data, ensuring transparent and trustworthy decision-making is essential. In skin cancer diagnosis, while advancements in lesion detection and classification have improved accuracy, the black-box nature of these methods poses challenges in understanding their decision processes, leading to trust issues among physicians. This study leverages the CLIP (Contrastive Language-Image Pretraining) model, trained on different skin lesion datasets, to capture meaningful relationships between visual features and diagnostic criteria terms. To further enhance transparency, we propose a method called MedGrad E-CLIP, which builds on gradient-based E-CLIP by incorporating a weighted entropy mechanism designed for complex medical imaging like skin lesions. This approach highlights critical image regions linked to specific diagnostic descriptions. The developed integrated pipeline not only classifies skin lesions by matching corresponding descriptions but also adds an essential layer of explainability developed especially for medical data. By visually explaining how different features in an image relates to diagnostic criteria, this approach demonstrates the potential of advanced vision-language models in medical image analysis, ultimately improving transparency, robustness, and trust in AI-driven diagnostic systems.

Figures

Figures reproduced from arXiv: 2501.06887 by the authors.

Figure 1
Figure 1. CLIP Overview for Custom Dataset: We encode skin lesion images and their descriptions to generate image and text em￾beddings. These are combined in a cross-modal interaction mod￾ule, calculating cosine similarities to assess alignment between le￾sions and diagnoses, ensuring accurate classification. Formally, let fx represent the image features extracted by CLIP’s image encoder for lesion image x. The text features,… view at source ↗
Figure 2
Figure 2. Proposed pipeline After training, the updated model weights were used to evaluate the model’s performance on the test dataset. To en￾hance the interpretability of the newly trained CLIP model, explainability techniques were applied, offering both visual and textual insights into the decision-making processes of the model. These explainability approaches were subse￾quently compared to evaluate their effectiveness. Ou… view at source ↗
Figure 3
Figure 3. Test accuracy and loss on pre-trained model [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Test accuracy and loss on fine-tuned model [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Comparative visualization of explainability methods—Original Atypical Nevus, Pre-trained Grad E-CLIP, Trained Grad E-CLIP, [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Comparative visualization of explainability methods—Original Melanoma, Pre-trained Grad E-CLIP, Trained Grad E-CLIP, [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]

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