Advancing Lung Disease Diagnosis in 3D CT Scans

Hao Chen; Jilan Xu; Junlin Hou; Qingqiu Li; Rui Feng; Runtian Yuan; Yuejie Zhang

REVIEW 4 major objections 5 minor 21 references

Advancing Lung Disease Diagnosis in 3D CT Scans

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Four-way lung CT classification reaches 0.80 Macro F1 with a 3D ResNeSt model.

desk verdict Single uncontextualized validation score with no baselines or error bars; the method is a competent composition of standard components, but 'strong performance' is not supported. read the letter →

arxiv 2507.00993 v1 pith:7B254OAW submitted 2025-07-01 eess.IV cs.CV

Qingqiu Li , Runtian Yuan , Junlin Hou , Jilan Xu , Yuejie Zhang , Rui Feng , Hao Chen This is my paper

classification eess.IVcs.CV

keywords lungdiseaseclassification3DCTscansResNeSt50weightedcross-entropylossclassimbalanceMacroF1FairDiagnosisChallengecomputedtomography

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 a complete-3D approach to classifying chest CT scans into four categories: normal, adenocarcinoma, squamous cell carcinoma, and COVID-19. It claims that cutting away slices that contain no lung tissue, feeding the remaining volume through a 3D ResNeSt50 network, and weighting the cross-entropy loss toward rare classes yields a Macro F1 of 0.80 on the validation set of the Fair Disease Diagnosis Challenge. The authors present this as evidence that 3D volumes, rather than stacks of independently analyzed 2D slices, can support automated lung disease triage.

What carries the argument

The load-bearing machinery is the combination of three components: (1) a preprocessing step that detects and removes non-lung slices from each 3D volume, cutting computation and focusing the model on lesions; (2) a 3D ResNeSt50 backbone, a residual network whose blocks include split-attention that weights interactions across feature-map groups; and (3) a weighted cross-entropy loss $L_{\text{wCE}} = -\sum_{c=1}^{C} w_c \, y_c \log(\hat{y}_c)$, where class weights $w_c$ are chosen to up-weight underrepresented classes such as squamous cell carcinoma. Together these allow the whole volume, not individual 2D slices, to drive the classification decision.

What would settle it

Re-run the same pipeline on multiple random splits of the provided validation set, or obtain the challenge test labels and compute per-class F1; if the Macro F1 drops below roughly 0.75 on any split, or the squamous cell carcinoma F1 confidence interval overlaps the majority-class baseline, the paper's 'strong performance' conclusion fails.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is a pipeline: after resizing each CT series to (64,256,256) and clipping intensities to [0,1], the model discards slices without lung tissue, extracts features with a 3D ResNeSt50 backbone whose split-attention blocks let feature-map groups interact, and trains with a lesion-aware weighted cross-entropy loss. The reported outcome is a Macro F1 score of 0.80 on the challenge validation set, where Macro F1 averages per-class F1 across normal, adenocarcinoma, squamous cell carcinoma, and COVID-19. The paper treats this number as showing the model's strong ability to distinguish lung conditions.

Load-bearing premise

The quantitative claim rests on the validation-set Macro F1 being stable and meaningful, but with 155 volumes, only 25 in the rarest class, and no error bars, per-class breakdown, or separate test set, the 0.80 score could shift materially with the split or the official F1 aggregation rule.

Editorial extensions

If this is right

If the 0.80 Macro F1 holds on an independent test set, the same preprocessing and weighting recipe can be carried over to other volumetric medical classification tasks with rare classes.
Removing non-lung slices lowers the input dimensionality, so the model can train on fewer compute resources (the paper uses four RTX 3090 GPUs) than a full-volume model.
The weighted loss directly targets the squamous cell carcinoma class, so per-class F1 for that category should be the first number to inspect in any replication.
Treating CT as a single 3D volume instead of a bag of 2D slices is claimed to avoid the suboptimal performance of slice-based approaches.

Reading between the lines

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

The 0.80 Macro F1 is reported without error bars or a per-class breakdown; with only 25 squamous cell carcinoma validation cases, that rare-class F1 likely has a wide confidence interval, so the aggregate score may not be stable across splits.
The non-lung slice removal is a fixed heuristic; a model that learns which slices matter from data, or one tested across scanners with different fields of view, would reveal whether the heuristic itself is essential.
A direct comparison pairing this pipeline with the same ResNeSt50 trained on raw full volumes would isolate how much of the gain comes from preprocessing versus the backbone or the loss weighting.

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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

Short version: this is a challenge-participation write-up that reports one number — Macro F1 0.80 on the Fair Disease Diagnosis Challenge validation set — with no baseline, no ablation, no per-class breakdown, and no error bars. The method itself is a sensible composition of standard pieces: 3D volume input, removal of non-lung slices, ResNeSt50 backbone, class-weighted cross-entropy. That is fine for its apparent purpose as a workshop report, but the paper does not earn the 'strong performance' claim.

What's actually new: a single validation-set measurement on a public challenge dataset. That is a legitimate data point, and the preprocessing choice (dropping neck/abdomen slices) is a reasonable and common trick to cut compute and focus the model. The use of weighted cross-entropy for the rare squamous cell carcinoma class is appropriate. The writing is clear and the methodology is reproducible in principle, though no code or data are provided.

The soft spots: the whole conclusion rests on 0.80 on 155 validation volumes, with only 25 squamous cell carcinoma cases. The macro F1 is directly sensitive to that small class, and no confidence interval or per-class F1 is given. There is no comparison to a trivial baseline (e.g., majority class, 2D slice-based model, or a plain ResNet), so 'strong' is unsubstantiated. The loss weighting is described as 'lesion-aware' in the intro but Equation (1) is plain class-weighted cross-entropy, which is a small mismatch. Also, tuning preprocessing and weights on the validation set without a separate test set means the reported number is at risk of being optimistic; the authors do not mention using a held-out split.

The citation pattern is fine — lots of self-citations but that's typical in this subfield, and the COVID-19 detection work they cite is relevant.

Who this is for: someone tracking challenge entries or looking for a quick baseline reference on this dataset. It does not advance methodology. A serious referee could justify spending time on it if the venue accepts challenge-participation notes, but as a standalone research contribution it is thin. My recommendation: if this comes to a journal, treat it as a workshop note; require at least one baseline and per-class results before accepting, or accept as a short report with appropriate caveats. For a main-track conference, desk reject is reasonable.

Referee Report

4 major / 5 minor

Summary. The manuscript proposes a three-stage pipeline for four-class lung disease classification from 3D chest CT volumes: removal of non-lung slices, a 3D ResNeSt50 backbone, and a weighted cross-entropy loss intended to counteract class imbalance. The reported result is a Macro F1 of 0.80 on the validation split of the Fair Disease Diagnosis Challenge, and the paper states that this demonstrates strong performance. The evaluation consists of a single aggregate number, with no held-out test set, no per-class results, no confidence intervals, no baselines, and no ablations.

Significance. The proposed combination of components is plausible and the paper provides useful implementation details (volume resizing, augmentation, optimizer settings, batch size, epoch count) that would help others reproduce the pipeline. If the 0.80 Macro F1 were shown to be stable and favorably compared against alternatives, the recipe would be of practical interest for challenge-style lung-disease classification. However, the current evidence is too thin to support the stated conclusion: a single validation-set number, computed on a small cohort with a rare class, cannot by itself demonstrate 'strong performance' without error bars, per-class breakdowns, or comparators.

major comments (4)

[§4.3 and Table 1] The central claim rests entirely on a single Macro F1 of 0.80 computed on the validation set of 155 volumes. With only 25 squamous cell carcinoma cases (13 female, 12 male) in that split, the per-class F1 for the rare class has wide sampling uncertainty, and the macro average is directly sensitive to that component. The paper reports no confidence interval, no per-class F1 values, and no number of training runs or seeds, so the reader cannot judge whether 0.80 is a stable estimate or a fluctuation of a particular split and initialization.
[§4.3] The word 'strong' presumes a comparison that is never supplied. No baseline architecture, no variant without slice removal, no variant without weighted loss, and no reference to other challenge entries or published benchmarks are provided. Without a comparator, 0.80 is an uncontextualized number; adding even a small set of ablations (e.g., plain 3D ResNet, unweighted cross-entropy, or no slice removal) would make the claim falsifiable and interpretable.
[§2, Eq. (1)] The weighted cross-entropy loss is presented as a contribution, but the class weights w_c are never specified. The reader cannot tell how much weight was given to squamous cell carcinoma or whether the weights were derived from the class frequencies in Table 1. Moreover, there is no ablation isolating this component, so the paper does not provide evidence that the weighting, rather than the backbone or the preprocessing, contributes to the reported 0.80.
[§4.1 and §4.2] The slice-removal preprocessing is described only qualitatively. There is no specification of how non-lung slices are identified, how many slices are removed on average, or whether the threshold is fixed or scan-dependent. This matters because the method assumes that removed slices never contain diagnostically relevant tissue; if the rule occasionally removes apical or basal slices containing lesions, the evaluation could be biased. At minimum, the manuscript should quantify the removed slice range and validate the assumption on the training set.

minor comments (5)

[Figure 2] The figure labels 'ResNestBlock' and 'ResNest' use a different capitalization from the text's 'ResNeSt50'; please make the notation consistent throughout.
[Eq. (1)] The notation y, ŷ, y_c, and ŷ_c is not defined in detail; please state explicitly that y_c is the one-hot ground-truth component and ŷ_c is the predicted probability for class c.
[Table 1] The caption uses abbreviations 'A', 'Covid', and 'G' without explanation; the table would be clearer if the caption defined that A stands for adenocarcinoma, G for squamous cell carcinoma, and Covid for COVID-19.
[§4.2] The augmentation description 'random cropping on the vertical section to 64' is ambiguous given that volumes are already resized to depth 64 in §4.1; please clarify whether the crop is applied to the original depth before resizing or is a depth-preserving random crop.
[General] The paper does not state whether code or trained models will be made available; adding a code-availability statement would improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the reported 0.80 Macro F1 is an empirical evaluation metric on held-out validation data, and no claimed prediction reduces to a fitted input or self-citation by construction.

full rationale

The paper contains no derivation chain in which a predicted quantity is equivalent to a fitted input by construction. The central result is a Macro F1 score on the validation set of the Fair Disease Diagnosis Challenge (Section 4.3), computed by calculating F1 for each category and averaging (Section 4.2); it is an external evaluation metric applied to held-out data, not an output of the training objective. The weighted cross-entropy loss (Eq. 1) uses class weights w_c to upweight underrepresented classes, but these weights are training-loss design choices and the validation Macro F1 is not a function of w_c by construction. The preprocessing step (removing non-lung slices) and the ResNeSt50 backbone are standard engineering choices, not results derived from the evaluation metric. References [19] and [20] are prior works by the same authors on COVID-19 detection, and they are cited as background, not as load-bearing evidence for the 0.80 Macro F1 claim. No uniqueness theorem, ansatz-smuggling, or renaming of a known empirical pattern is present. Concerns about the absence of per-class F1 values, confidence intervals, or comparator baselines are correctness and robustness concerns, not circularity: a single uncontextualized validation number may be weak evidence, but it is not a derivation that reduces to its own inputs. Therefore the circularity score is 0.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The paper makes no derivation, so the ledger reflects an empirical pipeline: one free parameter (loss weights), three domain/preprocessing assumptions, and no new entities. All architectural and preprocessing ingredients are taken from prior work.

free parameters (1)

Class weights w_c in the weighted cross-entropy loss (Eq. 1) = not reported; described as based on class/lesion-volume proportions
The paper states the weights mitigate imbalance for squamous cell carcinoma but never gives their formula or values; the reported Macro F1 is conditional on this hand-chosen or proportion-derived weighting.

assumptions (3)

domain assumption 3D adaptation of ResNeSt50 preserves the split-attention benefits of the 2D architecture.
Section 4.2 says 'We utilize 3D ResNeSt50 as the backbone' with no architectural details or verification that the 3D version behaves as expected.
ad hoc to paper Removing slices identified as non-lung (neck, abdomen) never removes diagnostically relevant tissue.
Section 2 removes these slices because 'they do not contain lung tissue', but no algorithm or validation is given; mislabeled slices would change the input and the result.
domain assumption The challenge validation set annotations and the Macro F1 computation are correct and match the official evaluation.
Section 4.3 relies on a single validation-set number without citing the official metric definition or any label-quality check.

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Cite this review

Pith. "Pith review of Advancing Lung Disease Diagnosis in 3D CT Scans." pith.science (2026). https://pith.science/paper/7B254OAW

@misc{pith2026250700993,
  author       = {Pith},
  title        = {Pith review of: Advancing Lung Disease Diagnosis in 3D CT Scans},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7B254OAW}},
  note         = {Machine review of arXiv:2507.00993}
}

read the original abstract

To enable more accurate diagnosis of lung disease in chest CT scans, we propose a straightforward yet effective model. Firstly, we analyze the characteristics of 3D CT scans and remove non-lung regions, which helps the model focus on lesion-related areas and reduces computational cost. We adopt ResNeSt50 as a strong feature extractor, and use a weighted cross-entropy loss to mitigate class imbalance, especially for the underrepresented squamous cell carcinoma category. Our model achieves a Macro F1 Score of 0.80 on the validation set of the Fair Disease Diagnosis Challenge, demonstrating its strong performance in distinguishing between different lung conditions.

Figures

Figures reproduced from arXiv: 2507.00993 by the authors.

**Figure 1.** Samples of different lung diseases. complete 3D volume and remove non-lung regions that do not contribute to disease identification. We then employ ResNeSt50 [21] as the backbone feature extractor and design a weighted cross-entropy loss function based on the proportion of lesion volume to address class imbalance. Our main contributions are as follows: 1. We analyze the structural characteristics of 3D CT scans and… view at source ↗

**Figure 2.** Overview of our framework for lung disease diagnosis. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

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Reference graph

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