Barttender: An approachable & interpretable way to compare medical imaging and non-imaging data

Ayush Singla; Chirag J. Patel; Shakson Isaac

REVIEW 3 major objections 6 minor 23 references

Barttender: An approachable & interpretable way to compare medical imaging and non-imaging data

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Grayscale bars let deep learning compare X-rays with EHR data directly.

desk verdict Clean idea—tabular bars plus blank-image control—but the preprocessing may leak test set information, so the headline 'comparable performance' claim needs a fix before I'd trust it. read the letter →

arxiv 2411.12707 v1 pith:AYRTWJ6R submitted 2024-11-19 q-bio.QM cs.CV

Ayush Singla , Shakson Isaac , Chirag J. Patel This is my paper · ORCID

classification q-bio.QMcs.CV

keywords medicalimageanalysistabulardatadeeplearningexplainabilitygIoUCheXpertMIMICmultimodalmachine

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

Barttender is a way to put tabular health-record data on the same footing as medical images inside a deep learning model. It turns each non-imaging variable, such as age, lab values, or vital signs, into a grayscale bar, appends those bars to a chest X-ray, and trains a convolutional network on the combined image. A companion "Blank Barttender" appends the same bars to a blank image, so the difference in performance between the two isolates what the image adds. The paper reports that this approach matches the standard alternative, training XGBoost or logistic regression on scalar image biomarkers, while adding local and global explanations through a new metric called gIoU. A sympathetic reader would take away that imaging and tabular data can be compared directly, without losing image information to scalar summaries.

What carries the argument

The machinery is the grayscale-bar encoding of tabular data plus the gIoU aggregation rule. Each continuous variable is quantile-normalized to $[0,1]$ and drawn as a bar whose intensity runs from white to black; categorical variables are assigned spaced values, and missing values are drawn as a red bar so missingness itself is visible to the model. Bars are concatenated to a real X-ray (Image Barttender) or to a blank image (Blank Barttender), and the same DenseNet-121 is trained on both. The gIoU measure weights the mean attribution intensity in a region by the Intersection over Union ($\text{IoU} = |A \cap B|/|A \cup B|$) between the model's high-attention mask and that region, then averages over the test set. This turns pixel-level saliency maps into a population-level coefficient-like importance score, and the same construction at the sample level yields local explanations.

What would settle it

Re-run the MIMIC workflow with quantile normalization and the logistic-regression feature selection performed separately inside each training fold. If the Blank Barttender no longer matches or beats XGBoost on tabular data, or if the Image Barttender's advantage shrinks, the reported parity would be traceable to leakage rather than to the bar representation.

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Extended reading notes

Core claim

The central claim is that converting non-imaging tabular features into grayscale bars and appending them to medical images produces a deep learning model whose predictive performance is comparable to established scalar-based comparisons, while remaining interpretable. On the MIMIC cardiomegaly task, the Image Barttender reaches an AUC of $0.86 \pm 0.02$ and an MCC of $0.51 \pm 0.05$, close to XGBoost with image biomarkers and tabular data ($0.83 \pm 0.02$ AUC) and logistic regression with the same inputs ($0.88 \pm 0.00$ AUC). The Blank Barttender, which uses only the bars, outperforms XGBoost and logistic regression trained on the same tabular features on most metrics, which the authors suggest may reflect nonlinear interactions among bars that shallow models miss. On CheXpert, the Blank Barttender replicates the relative ranking of logistic regression across conditions, and the bars' ordering does not change performance, evidence that the model attends to the bars' content rather than their position. The paper also introduces gIoU, an IoU-scaled, test-set-averaged attribution intensity, as a global feature importance measure, and shows that even when the X-ray dominates globally, individual patients can still have high bar importances.

Load-bearing premise

The comparison is unbiased only if the quantile normalization of continuous variables and the selection of the eight tabular features via logistic regression Z-scores were fit on training data alone, with the test set excluded; the paper does not state that this was done.

Editorial extensions

If this is right

Researchers can compare the utility of imaging versus tabular data without building domain-specific pipelines that convert images into scalar biomarkers, shortening the analysis cycle.
The gap between Image and Blank Barttender performance gives a direct, model-controlled estimate of the added predictive value of images over risk factors.
gIoU offers a population-level feature importance ranking for image-based models, while sample-level IoU flags individual patients for whom tabular features drive the prediction.
Because missing values are encoded as a distinct bar color, the approach can model missing data without imputation and can surface missingness as a predictive signal.
The same bar-encoding recipe can be applied to other disease targets, such as diabetes or cardiovascular risk, where tabular risk scores and imaging could be compared.

Reading between the lines

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

A natural extension the authors leave implicit: because quantile normalization preserves only rank order, the Blank Barttender's edge over XGBoost suggests the convolutional network may be exploiting nonlinear interactions among bar regions; one could test this by training on bars whose values are randomly shuffled across features.
gIoU is defined for any region of an attribution map, not only bars, so it could serve as a general-purpose global importance metric for other image tasks where saliency maps are already computed.
The framework could be used as a cheap screening step before building multimodal fusion models: if the Image Barttender shows no gain over the Blank Barttender, investing in image-based deep learning for that task may be unwarranted.
A clinically meaningful next study would present the Image Barttender and its sample-level attributions to clinicians and measure whether it changes their interpretation of a prediction; the authors name this as future work but do not test it.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The core idea is simple and well-presented: encode tabular EHR features as grayscale bars appended to the X-ray, train the same CNN on that image and on a blank-image control, and compare utilities. The blank control is a genuinely nice experimental touch—it isolates the tabular signal from the imaging signal. And the paper does a good job of positioning itself against prior tabular-to-image encodings (Zhu, Damri, Medeiros Neto, Alkhodari), with code and public data available. The gIoU aggregation is a reasonable descriptive tool, and the authors are appropriately careful that it supports correlation claims, not causal ones. For a proof-of-concept, the results are plausible: image Barttenders beat blank ones, and blank performance roughly tracks logistic regression and XGBoost.

The soft spots are real but mostly fixable. The biggest one is the preprocessing leakage that the stress-test flagged. Section 3.2 says feature selection uses roughly 22,000 samples, and the evaluation sample is 2,667. The text does not state that the 2,667 test samples were excluded from that feature-selection pool, and the natural reading is that they were included. Section 2.1 describes quantile normalization without saying the quantiles are fit on training data only, and Appendix B.2 only mentions holding out test samples for model training. So the reported numbers in Table 2 are likely in-sample with respect to feature selection and normalization. This affects all models equally (the same 8 features go into Barttender, XGBoost, and logistic regression), so the comparative ranking might survive, but the absolute out-of-sample performance and the generalizability claim are undermined. That needs to be clarified or corrected.

The 'comparable' language is also a bit loose. On CheXpert, the Blank Barttender AUC for Cardiomegaly is 0.53 versus logistic regression's 0.57—not obviously 'comparable' on AUC, though the MCC ranking is similar. And the encoding parameters (bar area fraction, attention threshold, blur kernel) are arbitrary and not swept, so sensitivity is unknown. That is a minor concern, not a fatal one.

Overall, this is a solid proof-of-concept for clinical ML researchers who want a quick, interpretable benchmark of imaging versus tabular utility. The contribution is modest but real, and the evaluation is honest in its limitations. I would send this to peer review with a request to fix the train/test separation in preprocessing and to be more precise about what 'comparable' means. If the leakage is corrected and the numbers hold, it would be a useful addition to the multimodal benchmarking toolbox.

Referee Report

3 major / 6 minor

Summary. The paper proposes Barttender, a framework that converts tabular EHR features into grayscale bars appended to medical images (Image Barttender) or to blank images (Blank Barttender), trains a DenseNet-121 on both, and compares the resulting models with logistic regression and XGBoost baselines trained on scalar representations. It introduces gIoU, an attribution-aggregation metric intended as a global feature importance measure for image-based models, and reports proof-of-concept experiments on CheXpert demographics and MIMIC cardiomegaly prediction. The central claim is that Barttender performs comparably to traditional scalar-based methods while offering local and global explainability.

Significance. If the evaluation is unbiased, Barttender is a useful contribution: it offers an approachable, explainable alternative for comparing imaging versus tabular utility, and it avoids the need for domain-specific image biomarker extraction. The paper is honest about limitations, including the absence of causal claims for gIoU and the need for clinical validation. Reproducibility is aided by the use of public datasets and released code, and the use of two attribution methods strengthens the explainability analysis. The main risk is procedural: the manuscript does not establish that normalization and feature selection are fit on training data alone, and this bears directly on the validity of the headline performance comparisons.

major comments (3)

[Section 3.2, 'Feature Selection' and Appendix B.2] The manuscript does not state whether quantile normalization (Section 2.1) and the logistic-regression feature selection are fit on training folds only. Since the MIMIC evaluation sample (n = 2667) is drawn from the same Duvieusart et al. cohort used to build the ≈22,000-sample feature-selection pool, the text as written does not rule out test information entering preprocessing. The statement in Appendix B.2 that 'test samples were held out across all folds' refers only to model training. If preprocessing was fit on the full cohort, the AUCs in Table 2 are in-sample estimates and the central 'comparable performance' claim is not supported. Please either state explicitly that all normalization and feature selection were nested inside the cross-validation loop, or re-run the pipeline accordingly and report the resulting metrics.
[Section 4.1 / Table 1] The claim that Blank Barttender 'has predictive performance similar to that of a logistic regression' is not fully supported by Table 1: for Cardiomegaly, Blank Barttender AUC is 0.53 ± 0.01 versus 0.57 ± 0.00 for logistic regression, a gap larger than the reported confidence intervals, while F1 and MCC rankings also differ (F1: 0.48 vs 0.38; MCC: 0.10 vs 0.07). The text should either report a statistical comparison of the AUC difference and justify the equivalence threshold, or soften the claim to 'similar on some metrics and some labels.'
[Section 2.3 / Section 5.1] The gIoU metric is computed from the model's own attribution maps, so it cannot independently validate that the model attends to the bars for the intended reason; a model that encodes a spurious shortcut in the bars would still produce high gIoU for those bars. The statement in Section 1 that 'the model learned robust features from the tabular data' therefore goes beyond what gIoU alone can show. Please add a sanity check, such as permuting or ablating individual bars and verifying that gIoU and model performance change in the expected direction, or compare gIoU rankings against a held-out feature-importance measure.

minor comments (6)

[Section 3.2] There is an unresolved reference 'Section ??' in the sentence 'As mentioned in Section ??'; this should be replaced with a specific pointer to the related work or to Duvieusart et al. (2022).
[Section 2.1 and Figure 1] There are several typos, including 'deep learing' in Section 2.1, 'prediciton' in Section 2.2, 'clincal' in Section 5, 'recaptitulated' in the introduction, and 'Urea Nitrogren' in Figure 1; these should be corrected.
[Table 3] The caption of Table 3 says 'computed using gIoU' but the table appears to report raw Integrated Gradients values; please clarify the relationship between the table entries and the gIoU formula.
[Appendix B.2] Please specify the optimizer, learning rate, early stopping criterion, and the exact horizontal/vertical placement and width of the bars (described only as 'about 20%' of the image); these details are needed for reproducibility.
[Appendix B.1] Appendix B.1 contains a self-reference to 'section B.1' in the sentence 'preprocessed as directed by Duvieusart et al. (2022) (details can be found in the appendix, section B.1)'; this should point to the relevant subsection.
[Table 2] All logistic regression entries in Table 2 report ±0.00 for every metric; if these values are rounded to two decimals, please state the rounding rule or report more significant digits so that the confidence intervals are meaningful.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central performance comparison is empirical and benchmarked against external baselines.

full rationale

Barttender's main claim, that it performs comparably to logistic regression and XGBoost on scalar representations of imaging and tabular data, is an empirical comparison against external models and external image biomarkers (Duvieusart et al., 2022). The bar-encoding transformation is a preprocessing choice, not a derivation, and the reported AUC/F1/MCC values are measured outcomes rather than quantities defined by the method's own equations. The gIoU measure is an aggregation of the model's own attribution maps, but the paper does not use gIoU to derive the performance comparison; it is a post-hoc explanation tool, and the paper explicitly cautions that gIoU lacks the statistical and causal foundations of regression coefficients. No load-bearing self-citations appear: the reference list contains no works by the present authors, and the external datasets, baselines, and attribution methods are independently established. The potential MIMIC preprocessing and feature-selection leakage is a methodological validity concern, not a circular derivation: the selected 8 features are chosen by logistic-regression Z-scores on roughly 22,000 samples, but the final model weights are trained on held-out folds, and the reported test metrics are not equal to the feature-selection statistics by construction. Therefore, no circular step can be exhibited, and the appropriate score is 0.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim depends on hand-chosen bar encoding values, an arbitrary attention cutoff, and assumptions that quantile normalization preserves information, that a blank image is a neutral control, and that saliency and Integrated Gradients reflect the model's use of features. No new physical entities are introduced; gIoU is a metric, not an entity.

free parameters (4)

Categorical bar encoding values = sex: No=0.2, Yes=1.0; race: 0.33/0.66/0.99; smoking: 0.1/0.4/0.9
Chosen by hand in Section 2.1 to maximize visual discrimination; no data-driven justification, and they change the input distribution the CNN sees.
Attention mask threshold = 10% highest-intensity pixels
Chosen in Section 2.3 as 'say, 10%'; gIoU values depend on this arbitrary cutoff.
Bar area fraction = ~20% of image width
Appendix B.2 states bars comprise about 20% of the image; the relative area directly enters the IoU weighting in gIoU, so this choice affects all gIoU comparisons.
Gaussian blur kernel for attribution maps = unspecified
Section 2.3 recommends a minor Gaussian blur without giving sigma; the resulting mask and gIoU are sensitive to this unstated parameter.

assumptions (5)

domain assumption Quantile normalization preserves the predictive information in tabular features and maps ranks to absolute values.
Section 2.1 and the Discussion claim this; no experiment isolates information loss from the bar encoding.
domain assumption Blank Barttender (bars on a blank image) isolates the contribution of non-imaging data.
Section 2.2 assumes the blank image contributes no signal, so any performance is attributed to the bars.
domain assumption Saliency maps and Integrated Gradients provide meaningful estimates of pixel relevance for DenseNet-121.
Section 2.3 uses these attributions despite citing Tomsett et al. that such maps can be unreliable.
domain assumption ImageNet pretraining transfers to chest X-rays with appended bars.
Appendix B.2 uses DenseNet-121 pretrained on ImageNet for all experiments; no domain-specific pretraining or random-init baseline is reported.
ad hoc to paper The 8 features selected by logistic regression Z-scores are the appropriate tabular features.
Section 3.2 selects features using a logistic regression screen on about 22,000 samples; this selection is not theoretically justified and may exclude informative features or include test information.

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Cite this review

Pith. "Pith review of Barttender: An approachable & interpretable way to compare medical imaging and non-imaging data." pith.science (2026). https://pith.science/paper/AYRTWJ6R

@misc{pith2026241112707,
  author       = {Pith},
  title        = {Pith review of: Barttender: An approachable & interpretable way to compare medical imaging and non-imaging data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AYRTWJ6R}},
  note         = {Machine review of arXiv:2411.12707}
}

read the original abstract

Imaging-based deep learning has transformed healthcare research, yet its clinical adoption remains limited due to challenges in comparing imaging models with traditional non-imaging and tabular data. To bridge this gap, we introduce Barttender, an interpretable framework that uses deep learning for the direct comparison of the utility of imaging versus non-imaging tabular data for tasks like disease prediction. Barttender converts non-imaging tabular features, such as scalar data from electronic health records, into grayscale bars, facilitating an interpretable and scalable deep learning based modeling of both data modalities. Our framework allows researchers to evaluate differences in utility through performance measures, as well as local (sample-level) and global (population-level) explanations. We introduce a novel measure to define global feature importances for image-based deep learning models, which we call gIoU. Experiments on the CheXpert and MIMIC datasets with chest X-rays and scalar data from electronic health records show that Barttender performs comparably to traditional methods and offers enhanced explainability using deep learning models.

Figures

Figures reproduced from arXiv: 2411.12707 by the authors.

**Figure 1.** Barttender overview. First, non-imaging tabular EHR features are converted into grayscale bars. Second, these bars are appended to medical images and blank images to create Image and Blank Barttenders respectively. Third, image-based deep learning models are trained on these Barttenders. These models can be used to evaluate differences in utility between EHR and imaging data for a task through performance measures … view at source ↗

**Figure 2.** Raincloud plot, which combines a scatter, box, and density plot, of gIoUs for Image Barttenders computed using gradientbased saliency maps. dicating that any signal in these features is replicated by the bar. This finding validates our methodology for transforming tabular features into images using grayscale bars. Barttenders are order-invariant All Barttender metrics reported in [PITH_FULL_IMAGE:figures/full_fig… view at source ↗

**Figure 3.** Raincloud plot of gIoUs for Blank Barttenders computed using saliency maps. Figures 2 and 3 offer insights into the distribution of the mean pixel intensity scaled by the IoU across the test set for Image and Blank Barttenders respectively. When the X-ray image is included ( [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

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Reviewed August 12, 2026 · model on record in the stance chip above.

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Khandoker, Herbert F

Mohanad Alkhodari, Ahsan H. Khandoker, Herbert F. Jelinek, Angelos Karlas, Stergios Soulaidopoulos, Petros Arsenos, Ioannis Doundoulakis, Konstantinos A. Gatzoulis, Konstantinos Tsioufis, and Leontios J. Hadjileontiadis. Circadian assessment of heart failure using explainable deep learning and novel multi-parameter polar images. Computer Methods and Progr...

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