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arxiv: 2604.09710 · v1 · submitted 2026-04-08 · 💻 cs.CV · cs.LG

Robust Fair Disease Diagnosis in CT Images

Justin Li , Daniel Ding , Asmita Yuki Pritha , Aryana Hou , Xin Wang , Shu Hu This is my paper

Pith reviewed 2026-05-10 18:25 UTC · model grok-4.3

classification 💻 cs.CV cs.LG
keywords fair disease diagnosisCT imagesclass imbalancedemographic fairnessCVaR aggregationlogit-adjusted loss3D ResNetmedical imaging
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The pith

A two-level loss combining logit-adjusted cross-entropy and CVaR improves both accuracy and fairness in CT-based disease diagnosis.

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

The paper establishes that deep learning models for automated chest CT diagnosis fail more severely when class imbalance and demographic underrepresentation coincide, creating compound bias that standard techniques cannot resolve. It introduces a training objective with logit-adjusted cross-entropy to adjust sample-level decision margins according to class frequency and CVaR aggregation to focus on the demographic group with the current highest loss. Evaluation on the Fair Disease Diagnosis benchmark, which includes extreme cases like five female samples for squamous cell carcinoma, shows the combined method reaches a gender-averaged macro F1 of 0.8403 and a fairness gap of 0.0239. This matters for clinical use because reliable diagnosis across patient groups is essential to avoid worsening health disparities.

Core claim

The paper claims that integrating logit-adjusted cross-entropy loss at the sample level with Conditional Value at Risk aggregation at the group level produces a robust and fair classifier for CT image diagnosis. On the benchmark dataset with four disease classes and sex annotations, this yields a 13.3% higher macro F1 score and a 78% smaller fairness gap compared to the baseline, with ablations confirming both components are necessary.

What carries the argument

The two-level objective: logit-adjusted cross-entropy loss that shifts decision margins proportionally to class frequency, paired with CVaR aggregation that directs optimization toward the worst-performing demographic group.

Load-bearing premise

The compound imbalance patterns observed in the Fair Disease Diagnosis benchmark, including extreme underrepresentation in specific disease and sex combinations, are representative of those encountered in broader clinical deployments.

What would settle it

Re-evaluating the method on a new CT dataset with different disease class frequencies and demographic distributions that shows no reduction in fairness gap or even an increase in disparity for some groups would indicate the approach does not reliably address compound bias.

Figures

Figures reproduced from arXiv: 2604.09710 by Aryana Hou, Asmita Yuki Pritha, Daniel Ding, Justin Li, Shu Hu, Xin Wang.

Figure 1
Figure 1. Figure 1: Training set distribution by disease category and pa￾tient sex. The dataset exhibits a compound imbalance: squamous cell carcinoma is already the rarest class overall (84 samples), and its female subset contains only 5 training examples. This intersec￾tion of class rarity and demographic scarcity motivates our two￾level objective that simultaneously addresses both axes of imbal￾ance. than 1.8 million death… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed framework. Row 1: representative CT slices from the four diagnostic categories. Row 2: preprocess￾ing removes non-pulmonary regions, samples to a fixed depth of 64, and normalizes to produce the input volume. Row 3: a Kinetics-400 pretrained R3D-18 extracts features, classified through a fully connected head. The logit adjusted loss (§ 3.4) corrects class imbalance, while CVaR aggr… view at source ↗
Figure 3
Figure 3. Figure 3: Performance vs. fairness trade-off across all configura [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
read the original abstract

Automated diagnosis from chest CT has improved considerably with deep learning, but models trained on skewed datasets tend to perform unevenly across patient demographics. However, the situation is worse than simple demographic bias. In clinical data, class imbalance and group underrepresentation often coincide, creating compound failure modes that neither standard rebalancing nor fairness corrections can fix alone. We introduce a two-level objective that targets both axes of this problem. Logit-adjusted cross-entropy loss operates at the sample level, shifting decision margins by class frequency with provable consistency guarantees. Conditional Value at Risk aggregation operates at the group level, directing optimization pressure toward whichever demographic group currently has the higher loss. We evaluate on the Fair Disease Diagnosis benchmark using a 3D ResNet-18 pretrained on Kinetics-400, classifying CT volumes into Adenocarcinoma, Squamous Cell Carcinoma, COVID-19, and Normal groups with patient sex annotations. The training set illustrates the compound problem concretely: squamous cell carcinoma has 84 samples total, 5 of them female. The combined loss reaches a gender-averaged macro F1 of 0.8403 with a fairness gap of 0.0239, a 13.3% improvement in score and 78% reduction in demographic disparity over the baseline. Ablations show that each component alone falls short. The code is publicly available at https://github.com/Purdue-M2/Fair-Disease-Diagnosis.

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 / 3 minor

Summary. The manuscript proposes a two-level training objective for fair and robust disease diagnosis from chest CT scans. At the sample level, it employs logit-adjusted cross-entropy loss to handle class imbalance with claimed consistency guarantees. At the group level, it uses Conditional Value at Risk (CVaR) to focus optimization on the worst-performing demographic group. Evaluated on a benchmark with severe compound imbalance (e.g., only 5 female samples for squamous cell carcinoma), the combined method achieves a gender-averaged macro F1 score of 0.8403 and a fairness gap of 0.0239, improving 13.3% over baseline F1 and reducing disparity by 78%. Ablation studies indicate both components are necessary, and the code is released publicly.

Significance. If the quantitative improvements are confirmed to be statistically significant and generalizable, the work would meaningfully advance methods for mitigating compound biases (class and demographic) in medical imaging, a critical issue for equitable AI in healthcare. The public availability of the code facilitates reproducibility and further testing, which is a positive aspect of the submission.

major comments (2)
  1. Abstract: The reported performance (gender-averaged macro F1 of 0.8403 with fairness gap 0.0239) and improvements (13.3% and 78%) are given as single point estimates without accompanying variance, error bars, or multi-run statistics. Given the extreme subgroup size of only 5 female squamous cell carcinoma samples in training, these metrics are prone to high variance from sampling or initialization; this directly affects the reliability of the central claim that the two-level objective outperforms the baseline.
  2. Method description (logit-adjusted loss): The abstract states that the logit-adjusted cross-entropy 'operates at the sample level, shifting decision margins by class frequency with provable consistency guarantees,' but the manuscript provides no derivation, proof outline, or citation specifying the conditions for these guarantees. This is important because the overall contribution relies on the combination with CVaR, and it is unclear if the guarantees hold in the presence of group-level risk aggregation.
minor comments (3)
  1. Abstract: The exact definition of the 'fairness gap' (e.g., whether it is the difference in per-group F1 scores or another metric) should be clarified explicitly, even if standard.
  2. Dataset description: A table summarizing the per-class, per-group sample counts in train/val/test splits would help readers assess the compound imbalance without referring to external code or the abstract alone.
  3. Training details: Hyperparameters such as the CVaR quantile level, learning rate, and number of epochs for the 3D ResNet-18 are not detailed in the text; while the code is available, a brief description in the paper would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which helps improve the clarity and rigor of our work. We address each major comment below.

read point-by-point responses

  1. Referee: Abstract: The reported performance (gender-averaged macro F1 of 0.8403 with fairness gap 0.0239) and improvements (13.3% and 78%) are given as single point estimates without accompanying variance, error bars, or multi-run statistics. Given the extreme subgroup size of only 5 female squamous cell carcinoma samples in training, these metrics are prone to high variance from sampling or initialization; this directly affects the reliability of the central claim that the two-level objective outperforms the baseline.

    Authors: We agree that single-point estimates are insufficient given the small subgroup sizes and potential variance from initialization or sampling. In the revised manuscript, we will rerun the experiments with at least five different random seeds, reporting mean and standard deviation for the key metrics (gender-averaged macro F1 and fairness gap). We will also add a statistical significance analysis (e.g., paired t-test against baseline) to support the reported improvements. revision: yes

  2. Referee: Method description (logit-adjusted loss): The abstract states that the logit-adjusted cross-entropy 'operates at the sample level, shifting decision margins by class frequency with provable consistency guarantees,' but the manuscript provides no derivation, proof outline, or citation specifying the conditions for these guarantees. This is important because the overall contribution relies on the combination with CVaR, and it is unclear if the guarantees hold in the presence of group-level risk aggregation.

    Authors: The logit-adjusted loss is adopted from prior work on imbalanced classification, where consistency guarantees (convergence to the class-frequency-adjusted Bayes classifier) have been established under standard assumptions as sample size grows. We will add the appropriate citation and a concise outline of the relevant theoretical result to the method section. The CVaR component aggregates risks across groups but does not modify the per-sample loss; the guarantees therefore continue to apply to the sample-level term, while the overall method is validated empirically. We will explicitly clarify this separation in the revision. revision: yes

Circularity Check

0 steps flagged

No circularity in empirical method and benchmark results

full rationale

The paper presents a two-level loss (logit-adjusted CE at sample level plus CVaR at group level) and directly measures its effect via macro F1 and fairness gap on the Fair Disease Diagnosis benchmark. These quantities are computed from model outputs on held-out test data; they are not defined in terms of the training parameters or inputs, nor do any equations reduce the reported 0.8403 / 0.0239 figures to the training distribution by construction. No self-definitional, fitted-input-renamed-as-prediction, or self-citation-load-bearing steps appear in the derivation chain. The evaluation is therefore self-contained against an external benchmark.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The approach rests on standard supervised learning assumptions plus the empirical claim that the chosen benchmark exhibits the targeted compound skew. No new entities are postulated.

axioms (2)
  • domain assumption Logit-adjusted cross-entropy provides consistent classification under class imbalance
    Invoked to justify the sample-level term; consistency guarantees are asserted but not derived in the abstract.
  • domain assumption CVaR aggregation directs optimization toward the worst-performing demographic group
    Core justification for the group-level term; relies on the risk-measurement literature without re-derivation.

pith-pipeline@v0.9.0 · 5561 in / 1331 out tokens · 30680 ms · 2026-05-10T18:25:34.728646+00:00 · methodology

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Reference graph

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