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REVIEW 4 major objections 5 minor 1 cited by

Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The TDSC-ABUS challenge supplies the first open benchmark that jointly scores tumor detection, segmentation, and classification on 200 automated 3D breast ultrasound volumes, and it reports rankings of 17 validated algorithm submissions.

desk verdict A genuinely useful ABUS benchmark that needs editorial cleanup and a more careful limitations section before its leaderboard should be treated as definitive. read the letter →

arxiv 2501.15588 v1 pith:PQWRBDGJ submitted 2025-01-26 eess.IV cs.CV

Gongning Luo , Mingwang Xu , Hongyu Chen , Xinjie Liang , Xing Tao , Dong Ni , Hyunsu Jeong , Chulhong Kim
show 29 more authors
Raphael Stock Michael Baumgartner Yannick Kirchhoff Maximilian Rokuss Klaus Maier-Hein Zhikai Yang Tianyu Fan Nicolas Boutry Dmitry Tereshchenko Arthur Moine Maximilien Charmetant Jan Sauer Hao Du Xiang-Hui Bai Vipul Pai Raikar Ricardo Montoya-del-Angel Robert Marti Miguel Luna Dongmin Lee Abdul Qayyum Moona Mazher Qihui Guo Changyan Wang Navchetan Awasthi Qiaochu Zhao Wei Wang Kuanquan Wang Qiucheng Wang Suyu Dong
This is my paper · ORCID
classification eess.IVcs.CV
keywords automatedbreastultrasoundABUStumordetectionsegmentationclassificationbenchmarkchallengecomputer-aideddiagnosisdeeplearning
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

This paper reports the first public benchmark that covers all three core computer-aided diagnosis tasks - tumor detection, segmentation, and classification - on automated 3D breast ultrasound (ABUS) images, and it uses that benchmark to compare 17 algorithms that completed a standardized evaluation. The organizers built the benchmark from 200 ABUS volumes with expert-drawn tumor boundaries and benign/malignant labels, split into 100 training, 30 validation, and 70 test cases, and ran it as an international challenge with independent scoring per task plus a combined leaderboard. The result the authors are trying to establish is that a fixed, openly accessible ABUS dataset and evaluation protocol can serve as a common yardstick for future research. A sympathetic reading sees the contribution as the benchmark itself: the paper's descriptions of winning strategies are secondary to the public platform they document.

What carries the argument

The carrying object is the challenge protocol itself: 200 three-dimensional ABUS volumes with known voxel spacings, radiologist-drawn tumor masks, and benign/malignant labels, with detection bounding boxes derived from those masks. Three independent metrics are normalized by min-max scaling and combined by fixed formulas; for instance, the segmentation score is (1 + normalized Dice - normalized Hausdorff)/2, and the overall score sums the three task scores, with infinite Hausdorff values replaced by 105% of the worst valid value. Each final submission ran inside a Docker container in a standardized environment, so the leaderboard reflects the algorithms rather than their host machines. This protocol is what turns a collection of images into a benchmark: it fixes the data split, the hit criterion (a detection counts when its box has IoU over 0.3 with ground truth), and the ranking rule.

What would settle it

Take the same 17 validated algorithm containers to an independent multi-center ABUS dataset with second-opinion radiologist labels and recompute all scores; if the leaderboard order changes substantially or the Dice-versus-Hausdorff ordering flips, the benchmark's claim to be a definitive yardstick is refuted.

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Extended reading notes

Core claim

The central claim is that the TDSC-ABUS challenge provides the first standard benchmark for assessing ABUS computer-aided diagnosis algorithms on detection, segmentation, and classification together. The authors argue this matters because ABUS images are hard for algorithms - tumors are small relative to the volume, boundaries are blurred, lesion-to-background similarity is high, and no public well-labeled dataset existed to compare methods fairly. On this dataset the best combined score was achieved by a pipeline of three specialized networks, one per task, while several leading segmentation entries built on U-Net-style architectures with patch-based training and ensembles; detection was frequently handled by deriving bounding boxes from segmentation output. The evaluation protocol scores segmentation with Dice and Hausdorff distance, classification with accuracy and area under the ROC curve, and detection with the free-response ROC curve averaged over false-positive levels from 0.125 to 8 per scan. The paper's thesis is that this combination of public data, metrics, and containerized testing makes algorithm rankings reproducible and gives future work a concrete baseline to beat.

Load-bearing premise

The whole benchmark rests on 200 scans from one hospital with one imaging protocol and labels drawn by radiologists from that setting, so if the case mix, scanner, or boundaries are not representative of other clinics, the rankings will not transfer there.

Editorial extensions

If this is right

  • Future ABUS algorithms can be compared against a public, fixed test set instead of private data, making published gains auditable.
  • Because the combined leaderboard ranks only teams that solve all three tasks, the benchmark creates pressure to build integrated pipelines rather than single-task solutions.
  • Segmentation quality must be reported with both overlap and boundary accuracy; the results show that a high Dice score does not imply a low Hausdorff distance.
  • Detection performance is better summarized by the FROC curve across false-positive rates than by a single accuracy number, since lesions occupy a small fraction of each volume.
  • Segmentation-guided detection and patch-based training are the strategies that most consistently worked across leading entries.

Reading between the lines

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

  • If the organizers add healthy cases and multi-center data, as the paper says they plan to, the current leaderboard should be re-run; rankings that hold up would be robust, while shifting rankings would show the 2023 results were partly artifacts of a single scanner and reader pool.
  • The paper's 105%-of-worst penalty for infinite Hausdorff distances is one of several defensible choices, so the leaderboard should be read as a comparison under that specific rule rather than an absolute ordering of methods.
  • The finding that the best combined performance came from three specialized models rather than one end-to-end network suggests that, at this data size, task-specific inductive biases still beat joint training; a future multi-task architecture might challenge that conclusion.
  • A future challenge could score runtime and resource use; the paper notes it did not, so reported performance does not yet distinguish clinically deployable methods from heavy ensembles.
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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 / 5 minor

Summary. This paper reports the organization, results, and analysis of the TDSC-ABUS 2023 challenge, a MICCAI-affiliated competition for tumor detection, segmentation, and classification in automated 3D breast ultrasound (ABUS). The authors describe a dataset of 200 ABUS volumes with radiologist-derived annotations, the challenge protocol (training, validation, and Docker-based test phases), the evaluation metrics (FROC for detection, Dice/HD for segmentation, ACC/AUC for classification), and the ranking scheme. The paper summarizes the algorithms of the participating teams and discusses common strategies and limitations. The central contribution claimed is an inaugural public benchmark combining the three tasks for ABUS CAD assessment.

Significance. If the inconsistencies and evaluation limitations are properly addressed, this is a useful community resource: the released dataset addresses a genuine lack of publicly available labeled ABUS data, and the challenge framework with held-out test evaluation and per-case disclosure is a sound basis for future benchmarking. The paper includes a large international participation (106 approved teams, 17 qualified submissions) and documents a broad range of technical approaches. However, the strength of the benchmark claims is currently undercut by the absence of tumor-free volumes in the detection evaluation and by several internal numerical and naming inconsistencies that must be resolved before the results can be considered reliable.

major comments (4)
  1. [Section 3.1, Section 3.2, Table 2] The reported number of participating teams is internally inconsistent. Section 3.1 states that 17 qualified submissions were obtained; Section 3.2 says "We have 17 teams that produced valid results, but one of them did not submit short paper... Thus we summarize key point of other 16 teams," and yet the text immediately proceeds to describe 18 distinct teams (T1 through T18). Table 2 is titled "Summary of the benchmark methods of top ten teams." These discrepancies make it impossible for the reader to know how many teams actually contributed to each leaderboard and which algorithms are included in each summary. This is load-bearing because the manuscript's contribution is a documented benchmark and leaderboard, not just a set of algorithm descriptions.
  2. [Section 2.1, Section 2.3, Section 4.5] The detection task is evaluated with FROC, which averages sensitivity over false-positive rates per scan, but the dataset contains no tumor-free ABUS volumes (Table 1 shows only malignant and benign cases; Section 4.5 concedes the dataset "lacked representation of other abnormalities or healthy tissue"). With all test scans containing at least one lesion, the FROC false-positive rate can only reflect false detections within tumor-bearing volumes, and cannot measure the detector's tendency to fire on healthy tissue. For a screening-oriented CAD benchmark, this omission is consequential: a non-specific detector that always outputs a candidate region would not be penalized for false positives on normal scans because no such scans exist. The limitation is acknowledged in Section 4.5, but the central claim in Section 1 of a "pioneering benchmark for ABUS CAD algorithm assessment" is not tempered accordingly, and the detection leaderboard is presented without this caveat in Section 3.3.3. The authors should either include tumor-free test volumes (or explicitly state that the detection benchmark is limited to lesion-positive volumes) and revise the wording of the benchmark claim.
  3. [Section 3.3.2] The classification results text says "with Team Shiontao clearly leading, particularly in AUC," but no team with this name appears in Table 5 or anywhere else in the manuscript. The leading team in Table 5 is T1 (SZU). This appears to be a leftover from another challenge or an editing artefact, and it directly confuses the reported ranking. The sentence should be corrected or the team name mapped to the table entry.
  4. [Section 3.3.1, Tables 3 and 4] The relationship between the unpenalized and penalized segmentation leaderboards is not explained clearly. Table 3 lists 10 teams with finite HD values; Table 4, after the 105% penalty for infinite HD, lists 14 teams, adding T8, T10, T1, and T4. The text does not state which teams had "inf" HD scores, why some teams appear only in Table 4, or how the per-case replacement of "inf" with 105% of the worst valid HD score affects the normalization step. Without this information, the segmentation ranking—which is a core result—is not reproducible from the tables as presented.
minor comments (5)
  1. [Article header / Front matter] The keywords block in the article front matter reads "Segmentation, Pulmonary artery, Multi-level, Efficiency," which is unrelated to the paper's content on breast ultrasound and appears to be a template leftover. The keywords should be replaced with appropriate ABUS/challenge-related terms.
  2. [Figure 10] Figure 10 includes teams T8 and T9 in the scatter plot, but Table 5 lists only 8 classification teams and does not include T8 or T9. The figure and table should be reconciled, or the figure legend should clarify which teams are shown.
  3. [Abstract] The abstract states that ABUS has advantages "over handheld mammography," which appears to be a typo for "handheld ultrasound" (or should be phrased differently). Mammography and ultrasound are distinct modalities, and the comparison as written is implausible.
  4. [Section 4.5] The text says label noise in the training dataset "was corrected in the test set to ensure fairness," but the paper does not describe how this correction was performed or how the test set labels were verified. A brief explanation (e.g., re-annotation protocol, number of cases) would improve transparency.
  5. [Figure 11] The x-axis label of Figure 11 says "False Positives per Image (FPPI)," while the text consistently refers to false positives per scan. The terminology should be aligned to avoid ambiguity about whether a "scan" and an "image" are the same unit in the FROC computation.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: TDSC-ABUS is a challenge report whose benchmark is supported by held-out test evaluation; the FROC/no-healthy-cases concern is a validity limitation, not a circular derivation.

full rationale

This paper is a challenge report rather than a derivation, and its central claim is empirical: a dataset, protocol, and leaderboard are offered as a benchmark. The rankings are computed from raw metrics (DICE, HD, ACC, AUC, FROC) obtained by running submitted Docker containers on a held-out 70-case test set whose labels were retained by the organizers (Sections 2.2 and 3.3). No parameter is fitted to the test labels, and no reported result is defined in terms of the conclusion it is said to support. The min-max normalization, the 0.3 IoU hit threshold, and the 105% penalty for infinite HD are organizer-defined scoring conventions; they define what 'winning' means by construction, but the underlying measured quantities are independent of those conventions, so this is not a fitted input renamed as a prediction. The skeptic's concern is real but is not circularity: Table 1 shows the 70 test cases are all malignant (40) or benign (30), and Section 4.5 concedes the dataset 'lacked representation of other abnormalities or healthy tissue.' Because FROC measures false positives per scan, the absence of tumor-free scans means false positives on normal breast tissue are never measured; this undermines the detection component of the 'pioneering benchmark' claim, but the metric is not defined in terms of the benchmark's conclusion, nor does any equation reduce to its own input. No load-bearing self-citation chain was found: citations to Maier-Hein et al. concern challenge reporting methodology, and participant-team descriptions cite external methods (nnU-Net, nnDetection, SAM, etc.) without using those citations to force the paper's conclusions. Under the hard rule that circularity must be exhibited as a specific reduction (Eq. X = Eq. Y by construction, or a fitted parameter renamed as a prediction), no circular step is present in this manuscript.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The evaluation design relies on organizer-chosen thresholds, normalization choices, and a penalty rule, while the benchmark's validity relies on label accuracy and cohort representativeness rather than on mathematical axioms.

free parameters (4)
  • FROC false-positive operating points = 0.125, 0.25, 0.5, 1, 2, 4, 8
    Organizer-chosen thresholds define the detection score; not derived from data.
  • Detection hit IoU threshold = 0.3
    A proposed box is counted as a hit only when its IoU with the ground-truth box exceeds 0.3; rankings depend on this choice.
  • Infinite HD penalty multiplier = 1.05
    Infinite Hausdorff distance values were replaced by 105% of the worst valid HD score, and this adjustment changes the top segmentation rank in Tables 3 and 4.
  • Min-max normalization across participant scores
    Applied per metric to scores of valid teams, making ranks relative to the weakest participant set rather than to an external fixed scale.
assumptions (3)
  • domain assumption Radiologist annotations define ground truth for tumor boundaries, boxes, and benign/malignant labels.
    Section 2.1 says images were annotated by an experienced radiologist, while Section 4.5 acknowledges label noise, so benchmark validity depends critically on annotation accuracy.
  • domain assumption Participant short papers accurately describe their algorithms and training procedures.
    Section 3.2 relies on team self-reports; no code or logs are verified by the organizers in the paper.
  • domain assumption The 200-case single-center cohort represents the target ABUS population for benchmarking.
    Section 4.5 notes limited imaging protocols and lack of healthy or multi-center cases, so generalizability is assumed rather than demonstrated.

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Cite this review

Pith. "Pith review of Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge." pith.science (2026). https://pith.science/paper/PQWRBDGJ

@misc{pith2026250115588,
  author       = {Pith},
  title        = {Pith review of: Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PQWRBDGJ}},
  note         = {Machine review of arXiv:2501.15588}
}
read the original abstract

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://tdsc-abus2023.grand-challenge.org/ to benchmark and inspire future developments in algorithmic research.

Figures

Figures reproduced from arXiv: 2501.15588 by the authors.

Figure 1
Figure 1. Representative ABUS Image Cases. (a) A small-sized tumor exhibiting [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Summary of TDSC-ABUS 2023 Challenge participants and submis [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. The model structure of T1 For classification, the team used a 3D ResNet architecture in CLSNet (He et al., 2016), which leverages softmax activation to differentiate between benign and malignant lesions. The 3D ResNet’s ability to capture complex spatial patterns in breast ul￾trasound images enhances the model’s precision in lesion char￾acterization. For segmentation, SZU built SEGNet on the traditional UNet archite… view at source ↗
Figures from the paper (7 more)
Figure 5
Figure 5. Figure 5: Network structure diagram of T11 (CNNs) for efficient 3D image segmentation. Their method uses depth-wise separable convolution for spatial and channel feature extraction and incorporates a transformer-based block with cross-attention in the encoder, while the decoder …
Figure 6
Figure 6. Figure 6: The proposed model for segmentation of T12 [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: The framework of T13 3.2.14. T14: Shanghai University (SHU) SHU developed a system for breast lesion analysis using three specialized models. They employed VNet (Milletari et al., 2016), which leverages skip connections and a dice coefficient loss function to improve s…
Figure 8
Figure 8. Figure 8: Figures illustrating the final scores of the teams that successfully completed all three tasks. (a) Radar chart showing individual performance across five [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: Visualization of segmentation task performance. Subfigures show (a) the DICE box plot, (b) the HD box plot, and (c) the line plot summarizing team scores. [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]
Figure 10
Figure 10. Figure 10: Classification task scatter plot, with Accuracy on the x-axis and Area Under the Curve on the y-axis. Each point represents a team. [PITH_FULL_IMAGE:figures/full_fig_p018_10.png]
Figure 11
Figure 11. Figure 11: FROC curve for the detection task, displaying sensitivity (recall) on the y-axis and False Positives Per Image (FPPI) on the x-axis. The x-axis is plotted [PITH_FULL_IMAGE:figures/full_fig_p019_11.png]

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    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.