Deep Learning for Pneumothorax Detection and Localization in Chest Radiographs

Andr\'e Goo{\ss}en; Axel Saalbach; Evan Schwab; Hrishikesh Deshpande; Ivo Baltruschat; Nathan Cross; Thusitha Mabotuwana; Tim Harder

arxiv: 1907.07324 · v1 · pith:BN43OR74new · submitted 2019-07-16 · 📡 eess.IV · cs.CV· cs.LG

Deep Learning for Pneumothorax Detection and Localization in Chest Radiographs

Andr\'e Goo{\ss}en , Hrishikesh Deshpande , Tim Harder , Evan Schwab , Ivo Baltruschat , Thusitha Mabotuwana , Nathan Cross , Axel Saalbach This is my paper

Pith reviewed 2026-05-24 20:44 UTC · model grok-4.3

classification 📡 eess.IV cs.CVcs.LG

keywords pneumothoraxchest radiographsdeep learningconvolutional neural networksmultiple instance learningfully convolutional networksdetectionlocalization

0 comments

The pith

Three deep learning methods detect pneumothorax in chest X-rays with AUCs of 0.96, 0.93 and 0.92.

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

The paper compares three deep learning approaches for identifying pneumothorax, a dangerous condition where air enters the space around the lungs, in chest radiographs. It evaluates convolutional neural networks, multiple-instance learning, and fully convolutional networks on a set of 1003 images using five-fold cross-validation. The methods achieve high area under the ROC curve scores, with the convolutional neural network performing best. An ensemble of the three is also reviewed for classification and localization performance. This work aims to support faster detection to improve patient outcomes in critical cases.

Core claim

On a dataset of 1003 chest X-ray images, convolutional neural networks achieve an AUC of 0.96, multiple-instance learning 0.93, and fully convolutional networks 0.92 for pneumothorax detection, with the approaches also demonstrating localization capabilities, and their ensemble reviewed for combined performance.

What carries the argument

The three deep learning techniques—convolutional neural networks, multiple-instance learning, and fully convolutional networks—applied to chest radiograph classification and localization.

If this is right

Early detection of pneumothorax becomes feasible through automated analysis of chest X-rays.
Localization of the condition can guide clinical attention to specific areas in the image.
An ensemble approach may improve overall reliability by combining the strengths of different methods.
Five-fold cross-validation provides a robust estimate of performance on the given dataset.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

These methods could be integrated into radiology workflows to flag urgent cases for immediate review.
Performance on this dataset suggests potential for reducing missed diagnoses in emergency settings.
Further validation on diverse patient populations would be needed to confirm generalizability.

Load-bearing premise

The 1003 chest X-ray images with labels are representative of real clinical cases without significant biases in selection or annotation.

What would settle it

A test on an independent dataset of chest X-rays from different hospitals or populations yielding substantially lower AUC values would disprove the claim of reliable detection.

Figures

Figures reproduced from arXiv: 1907.07324 by Andr\'e Goo{\ss}en, Axel Saalbach, Evan Schwab, Hrishikesh Deshpande, Ivo Baltruschat, Nathan Cross, Thusitha Mabotuwana, Tim Harder.

**Figure 2.** Figure 2: The proposed Multiple-Instance Learning architecture, using the CNN as [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: The proposed FCN architecture using a four-layer U-Net [ [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Averaged ROC curves over five splits for all methods and an ensemble. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: Localization compared to manual annotation for a normal and two pneu [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

read the original abstract

Pneumothorax is a critical condition that requires timely communication and immediate action. In order to prevent significant morbidity or patient death, early detection is crucial. For the task of pneumothorax detection, we study the characteristics of three different deep learning techniques: (i) convolutional neural networks, (ii) multiple-instance learning, and (iii) fully convolutional networks. We perform a five-fold cross-validation on a dataset consisting of 1003 chest X-ray images. ROC analysis yields AUCs of 0.96, 0.93, and 0.92 for the three methods, respectively. We review the classification and localization performance of these approaches as well as an ensemble of the three aforementioned techniques.

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.

Desk Editor's Note private letter to a colleague

The paper runs a head-to-head of three standard deep learning methods on pneumothorax detection and gets AUCs of 0.92-0.96, but the 1003-image dataset leaves the independence of the five-fold CV unverified.

read the letter

The results here rest on a dataset whose independence isn't clearly established, so the reported AUCs of 0.96, 0.93, and 0.92 need to be taken with that in mind. The abstract describes five-fold cross-validation on 1003 images but supplies no details on whether splits were done at the patient level or if there was an external hold-out set. What the paper does well is run a direct comparison of three different deep learning approaches—standard CNNs, multiple instance learning, and fully convolutional networks—on the same pneumothorax detection task, including localization performance and an ensemble. That kind of controlled comparison on a medical imaging problem is useful even if the techniques are not new. The soft spot is the validation design. If multiple images come from the same patients, image-level splitting would allow patient-specific information to cross folds and inflate the metrics. The lack of any mention of external validation or dataset provenance makes it difficult to assess how representative the numbers are for real-world use. This work is aimed at the medical imaging and radiology AI community. Readers looking for empirical results on pneumothorax detection will get a sense of what these methods can do, though they will likely want more rigorous testing before applying the findings. I would send it out for peer review. The comparison is worth referee attention, and the data concerns can be addressed in revision.

Referee Report

3 major / 2 minor

Summary. The manuscript compares three deep learning approaches—convolutional neural networks, multiple-instance learning, and fully convolutional networks—for pneumothorax detection and localization in chest radiographs. On a dataset of 1003 images, five-fold cross-validation yields reported AUCs of 0.96, 0.93, and 0.92 respectively; the work also evaluates an ensemble and reviews both classification and localization performance.

Significance. If the performance metrics prove robust under proper independent validation, the comparative evaluation of the three architectures plus ensemble could offer practical guidance for method selection in an urgent clinical task. The explicit attention to localization performance is a strength of the empirical design.

major comments (3)

[Dataset description (abstract and §3)] Dataset description (abstract and §3): the 1003-image collection is introduced without any information on institutional source, patient demographics, label acquisition protocol, class balance, or prevalence. This information is required to assess whether the reported AUCs can be interpreted as representative of clinical distributions.
[Experimental protocol (§4, cross-validation paragraph)] Experimental protocol (§4, cross-validation paragraph): no statement is made on whether the five-fold splits were performed at the patient level. If multiple images per patient exist and splits are image-level, patient-specific features can leak across folds, directly undermining the independence assumption that supports the headline AUC claims of 0.96/0.93/0.92.
[Results (§5)] Results (§5): the manuscript reports point AUC values without confidence intervals, statistical comparison between methods, or an external held-out test set. These omissions leave the relative ranking of the three methods and the ensemble unverified.

minor comments (2)

[Abstract] The abstract would be strengthened by a single sentence summarizing dataset size, source type, and any noted limitations.
[Methods] Notation for the three methods is introduced inconsistently between the abstract and the methods section; a single consistent abbreviation table would improve readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and indicate the revisions we will make to improve clarity and rigor.

read point-by-point responses

Referee: [Dataset description (abstract and §3)] Dataset description (abstract and §3): the 1003-image collection is introduced without any information on institutional source, patient demographics, label acquisition protocol, class balance, or prevalence. This information is required to assess whether the reported AUCs can be interpreted as representative of clinical distributions.

Authors: We agree that these details are essential for contextualizing the results. In the revised manuscript we will expand the dataset description in the abstract and Section 3 to include institutional source, patient demographics, label acquisition protocol, class balance, and prevalence. revision: yes
Referee: [Experimental protocol (§4, cross-validation paragraph)] Experimental protocol (§4, cross-validation paragraph): no statement is made on whether the five-fold splits were performed at the patient level. If multiple images per patient exist and splits are image-level, patient-specific features can leak across folds, directly undermining the independence assumption that supports the headline AUC claims of 0.96/0.93/0.92.

Authors: We thank the referee for highlighting this critical aspect of the experimental design. The five-fold cross-validation splits were performed at the patient level to prevent leakage of patient-specific features. We will add an explicit statement to this effect in the revised Section 4. revision: yes
Referee: [Results (§5)] Results (§5): the manuscript reports point AUC values without confidence intervals, statistical comparison between methods, or an external held-out test set. These omissions leave the relative ranking of the three methods and the ensemble unverified.

Authors: We agree that confidence intervals and statistical comparisons would strengthen the presentation. In the revised Section 5 we will report bootstrap confidence intervals for all AUC values and include pairwise statistical comparisons. An external held-out test set was not available within the scope of this study; we will add a limitations paragraph discussing the value of future external validation. revision: partial

Circularity Check

0 steps flagged

No circularity: purely empirical performance reporting with no derivations or fitted predictions

full rationale

The paper trains three deep learning models (CNN, MIL, FCN) on a fixed dataset of 1003 images and reports AUCs from 5-fold cross-validation. No equations, first-principles derivations, parameter fitting followed by prediction, or self-citation chains are present. The central claims are direct empirical measurements of model performance on held-out folds; they do not reduce to the inputs by construction. Dataset splitting concerns affect experimental validity but are unrelated to circularity in any derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no information on free parameters, axioms, or invented entities.

pith-pipeline@v0.9.0 · 5680 in / 1079 out tokens · 37369 ms · 2026-05-24T20:44:47.873730+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We study the characteristics of three different deep learning techniques: (i) convolutional neural networks, (ii) multiple-instance learning, and (iii) fully convolutional networks. We perform a five-fold cross-validation on a dataset consisting of 1003 chest X-ray images. ROC analysis yields AUCs of 0.96, 0.93, and 0.92

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

12 extracted references · 12 canonical work pages · 2 internal anchors

[1]

Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification

Baltruschat, I.M., Nickisch, H., Grass, M., Knopp, T., Saalbach, A.: Com- parison of deep learning approaches for multi-label chest X-ray classiﬁcation. arXiv:1803.02315 (2018)

work page internal anchor Pith review Pith/arXiv arXiv 2018
[2]

Artif Intell 89(1-2), 31–71 (1997)

Dietterich, T.G., Lathrop, R.H., Lozano-P´ erez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artif Intell 89(1-2), 31–71 (1997)

work page 1997
[3]

Nature 542(7639), 115 (2017)

Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S.: Dermatologist-level classiﬁcation of skin cancer with deep neural networks. Nature 542(7639), 115 (2017)

work page 2017
[4]

JAMA 316(22), 2402–2410 (2016) Pneumothorax Detection and Localization in Chest Radiographs 9

Gulshan, V., Peng, L., Coram, M., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus pho- tographs. JAMA 316(22), 2402–2410 (2016) Pneumothorax Detection and Localization in Chest Radiographs 9

work page 2016
[5]

In: Proc CVPR

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proc CVPR. pp. 770–778. IEEE (2016)

work page 2016
[6]

J Am Coll Radiol 11(6), 552–558 (2014)

Larson, P.A., Berland, L.L., Griﬃth, B., Kahn Jr., C.E., Liebscher, L.A.: Action- able ﬁndings and the role of IT support: report of the ACR actionable reporting work group. J Am Coll Radiol 11(6), 552–558 (2014)

work page 2014
[7]

Comput Biol Med 89, 135–143 (2017)

Lopes, U., Valiati, J.F.: Pre-trained convolutional neural networks as feature ex- tractors for tuberculosis detection. Comput Biol Med 89, 135–143 (2017)

work page 2017
[8]

Attention U-Net: Learning Where to Look for the Pancreas

Oktay, O., Schlemper, J., Folgoc, L.L., et al.: Attention U-Net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)

work page internal anchor Pith review Pith/arXiv arXiv 2018
[9]

In: International Conference on Medical image computing and computer-assisted intervention

Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomed- ical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. pp. 234–241. Springer (2015)

work page 2015
[10]

In: Proc CVPR

Wang, X., Peng, Y., Lu, L., et al.: ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classiﬁcation and localization of common thorax diseases. In: Proc CVPR. pp. 3462–3471. IEEE (2017)

work page 2017
[11]

IEEE T Med Imaging 35(5), 1332–1343 (2016)

Yan, Z., Zhan, Y., Peng, Z., et al.: Multi-instance deep learning: Discover discrim- inative local anatomies for bodypart recognition. IEEE T Med Imaging 35(5), 1332–1343 (2016)

work page 2016
[12]

Chest 141(4), 1098–1105 (2012)

Yarmus, L., Feller-Kopman, D.: Pneumothorax in the critically ill patient. Chest 141(4), 1098–1105 (2012)

work page 2012

[1] [1]

Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification

Baltruschat, I.M., Nickisch, H., Grass, M., Knopp, T., Saalbach, A.: Com- parison of deep learning approaches for multi-label chest X-ray classiﬁcation. arXiv:1803.02315 (2018)

work page internal anchor Pith review Pith/arXiv arXiv 2018

[2] [2]

Artif Intell 89(1-2), 31–71 (1997)

Dietterich, T.G., Lathrop, R.H., Lozano-P´ erez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artif Intell 89(1-2), 31–71 (1997)

work page 1997

[3] [3]

Nature 542(7639), 115 (2017)

Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S.: Dermatologist-level classiﬁcation of skin cancer with deep neural networks. Nature 542(7639), 115 (2017)

work page 2017

[4] [4]

JAMA 316(22), 2402–2410 (2016) Pneumothorax Detection and Localization in Chest Radiographs 9

Gulshan, V., Peng, L., Coram, M., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus pho- tographs. JAMA 316(22), 2402–2410 (2016) Pneumothorax Detection and Localization in Chest Radiographs 9

work page 2016

[5] [5]

In: Proc CVPR

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proc CVPR. pp. 770–778. IEEE (2016)

work page 2016

[6] [6]

J Am Coll Radiol 11(6), 552–558 (2014)

Larson, P.A., Berland, L.L., Griﬃth, B., Kahn Jr., C.E., Liebscher, L.A.: Action- able ﬁndings and the role of IT support: report of the ACR actionable reporting work group. J Am Coll Radiol 11(6), 552–558 (2014)

work page 2014

[7] [7]

Comput Biol Med 89, 135–143 (2017)

Lopes, U., Valiati, J.F.: Pre-trained convolutional neural networks as feature ex- tractors for tuberculosis detection. Comput Biol Med 89, 135–143 (2017)

work page 2017

[8] [8]

Attention U-Net: Learning Where to Look for the Pancreas

Oktay, O., Schlemper, J., Folgoc, L.L., et al.: Attention U-Net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)

work page internal anchor Pith review Pith/arXiv arXiv 2018

[9] [9]

In: International Conference on Medical image computing and computer-assisted intervention

Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomed- ical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. pp. 234–241. Springer (2015)

work page 2015

[10] [10]

In: Proc CVPR

Wang, X., Peng, Y., Lu, L., et al.: ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classiﬁcation and localization of common thorax diseases. In: Proc CVPR. pp. 3462–3471. IEEE (2017)

work page 2017

[11] [11]

IEEE T Med Imaging 35(5), 1332–1343 (2016)

Yan, Z., Zhan, Y., Peng, Z., et al.: Multi-instance deep learning: Discover discrim- inative local anatomies for bodypart recognition. IEEE T Med Imaging 35(5), 1332–1343 (2016)

work page 2016

[12] [12]

Chest 141(4), 1098–1105 (2012)

Yarmus, L., Feller-Kopman, D.: Pneumothorax in the critically ill patient. Chest 141(4), 1098–1105 (2012)

work page 2012