Modern Backbones Improve Multi-task DETR for Mammography Classification and Lesion Localization

Cao Truong Tran; Dac Phu Ho; Dinh Tan Nguyen; Lan T Ho-Pham; Le-Hoang Nguyen; Liem Pham; Minh-Toan Dinh; Nguyen Quoc Khanh Le; Quang-Hien Kha; Sai Ho Ling

REVIEW 4 major objections 5 minor 14 references

Modern Backbones Improve Multi-task DETR for Mammography Classification and Lesion Localization

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Modern backbone features improve joint mammography classification and lesion localization in a multi-task DETR framework, with ConvNeXtV2 and DINOv3 leading.

desk verdict Useful empirical backbone benchmark for mammography DETR, but the causal claim about architecture is undercut by pretraining and capacity confounds. read the letter →

arxiv 2608.09801 v1 pith:72C4NE4U submitted 2026-08-10 cs.CV cs.AI

Dinh Tan Nguyen , Quang-Hien Kha , Le-Hoang Nguyen , Minh-Toan Dinh , Xuan-Huy Nguyen , Dac Phu Ho , Cao Truong Tran , Sai Ho Ling

show 3 more authors

Lan T Ho-Pham Liem Pham Nguyen Quoc Khanh Le

This is my paper · ORCID

classification cs.CVcs.AI

keywords mammographymulti-tasklearningDETRlesionlocalizationbackbonecomparisonConvNeXtV2DINOv3breastcancerscreening

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 argues that in a multi-task DETR model for mammography, the choice of visual backbone is a decisive design factor: modern backbone features consistently outperform the older ResNet50 representation on both image-level malignancy classification and candidate-region localization. The authors test four backbones inside an otherwise fixed framework on OPTIMAM and the biopsy-confirmed SGM1k cohort, and report that ConvNeXtV2 and DINOv3 give the strongest overall results while MambaVision trails. The practical stake is that an AI decision-support system can supply both an exam-level risk score and spatially plausible pointers to suspicious regions, and that this joint capability depends heavily on the quality of the shared representation. The paper frames localization output as candidate-region support for review rather than precise lesion delineation.

What carries the argument

The load-bearing mechanism is the interchangeable-backbone multi-task DETR pipeline: a visual backbone whose output is projected through a 1×1 convolution into a common 256-channel feature map that feeds a global-average-pooling classification branch and a localization branch made of multi-scale dilated 3×3 convolutions feeding a Deformable DETR-style decoder with learned object queries. DETR, the DEtection TRansformer, is an end-to-end object detector that predicts a set of objects directly, and its deformable variant improves convergence and small-object handling. This design holds everything downstream fixed, so the only variable is which pretrained features the shared representation starts from. The joint objective sums an image-level classification loss with a detection loss combining bipartite matching, box regression, generalized IoU, and objectness supervision.

What would settle it

Train all four backbones from scratch on the two mammography datasets under the same protocol, or re-pretrain them with a single shared pretraining method; if the same ordering of ConvNeXtV2 and DINOv3 over ResNet50 and MambaVision does not appear, then the conclusion about backbone suitability is an artifact of pretraining differences.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that representation quality, not the detection head, dominates multi-task performance in mammography: exchanging only the backbone initialization inside a shared Deformable-DETR-style multi-task model changes both classification and localization outcomes substantially. Across both datasets modern backbones consistently beat ResNet50; on OPTIMAM, ConvNeXtV2 achieves 97.96% AUC, 99.89% sensitivity, 25.08% mAP@.5, and 74.38% recall@.25, while on SGM1k, DINOv3 reaches 90.97% AUC, 86.28% sensitivity, 27.04% mAP@.5, and 77.32% recall@.25. The authors conclude that backbone suitability is a key design factor and that ConvNeXtV2 appears particularly well matched to mammography's fine-grained patterns.

Load-bearing premise

All models are compared with identical training except backbone initialization, but the backbones were pretrained on different data with different objectives, so the performance gap may reflect pretraining recipe rather than architecture class.

Editorial extensions

If this is right

Backbone choice should be treated as a primary design axis, not an afterthought, when building multi-task mammography models.
A strong CNN such as ConvNeXtV2 can match or outperform a large self-supervised ViT on OPTIMAM, so transformer features are not automatically superior for mammography.
Detection outputs should be interpreted as candidate-region support; even approximate boxes and attention maps may help direct a radiologist's attention, especially in dense breasts where exact boundaries are hard.
The fixed multi-task framework provides a reusable benchmark for future backbones without retuning the detection head.

Reading between the lines

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

Editorial inference: because only backbone initialization differs across experiments, the observed ordering may be driven largely by pretraining data and objectives rather than by architecture class; a head-to-head test with shared pretraining would resolve this.
Editorial inference: the Grad-CAM alignment with clinically suspicious regions could be quantified and turned into a ranking metric for candidate proposals, giving a cheap proxy for localization quality.
Editorial inference: the same controlled-backbone protocol could be applied to dense-breast subgroups, where screening sensitivity is known to drop, to see whether backbone quality matters most in difficult cases.

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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

Colleague,

The short version: this is a useful, honest empirical benchmark, but the headline claim that backbone architecture is 'well-matched' to mammography goes beyond what the experiments can show. If you work on multi-task DETR for mammography, the numbers are worth having; if you are looking for a causal story about architecture families, this isn't it.

What's new: they plug four backbones (ResNet50, ConvNeXtV2-Tiny, MambaVision-Tiny, DINOv3 ViT-B/16) into a fixed Deformable DETR plus classification head, train on two patient-level split datasets, and report classification and localization metrics. The pipeline is standardized, code is public, and the tables are consistent with the prose. That is a legitimate contribution for a short-paper track.

The soft spots are real but not fatal. First, the four backbones differ in pretraining data, objective, and parameter count as much as in architecture. ResNet50 is ImageNet-supervised, ConvNeXtV2 uses masked autoencoders, DINOv3 is a large self-supervised ViT, and MambaVision has its own hybrid recipe. Appendix C says 'aside from backbone initialization, all settings were identical,' but that covers the downstream fine-tuning, not the representation being varied. So the observed ordering is descriptive: these off-the-shelf backbones, with their default pretraining, perform differently in this framework. The conclusion that ConvNeXtV2 is intrinsically well suited to mammography is not supported.

Second, there are no error bars or significance tests. On SGM1k, the AUC gap between DINOv3 and ConvNeXtV2 is only 0.53 points; without variance estimates we can't say it's a real difference. The 3-object-query cap is also an odd choice that likely limits detection, and there is no external baseline against prior mammography methods, so the absolute numbers only make sense within this framework.

That said, the relative ordering is plausible, the experiments were run carefully with patient-level splits, and the paper doesn't oversell the clinical reading: it explicitly frames detection as candidate-region support. The citation pattern is clean; the only self-citation is M2Net for motivation, not for the numbers. I'd like to see error bars and a more cautious causal claim before relying on the ranking, but as a benchmark it deserves a proper referee.

Verdict: worth engaging with. Send it to peer review; the revision should address the pretraining confound and add uncertainty estimates.

Referee Report

4 major / 5 minor

Summary. The paper studies a multi-task DETR framework for mammography that shares a visual backbone between image-level malignancy classification and query-based lesion localization. The authors compare four interchangeable backbones—ResNet50, ConvNeXtV2-Tiny, MambaVision-Tiny, and DINOv3 ViT-B/16—on OPTIMAM and a biopsy-confirmed SGM1k cohort, using a standardized training pipeline. They report that ConvNeXtV2 and DINOv3 give the strongest overall classification and localization results, with ConvNeXtV2 best on OPTIMAM and DINOv3 best on SGM1k, while MambaVision is less competitive. The paper concludes that backbone quality is a key determinant of multi-task mammography performance and that ConvNeXtV2 is particularly well matched to mammography.

Significance. If the reported ranking is robust, the paper provides a practically useful empirical guide for selecting off-the-shelf backbones in multi-task mammography models. Strengths include evaluation on two datasets with patient-level splits, a common downstream pipeline, released code, and a clinically relevant task formulation. The comparison is direct and internally consistent as a ranking of off-the-shelf backbones. However, the central mechanistic claim—that certain backbone families are intrinsically better matched to mammography—is not established because pretraining recipes, pretraining data, and model capacity vary together with architecture, and no uncertainty quantification is provided. The paper is a reasonable empirical study but currently overstates the causal reading of its results.

major comments (4)

[Section 2 and Appendix C] The claimed controlled comparison is not fully controlled on the variable of interest. Appendix C states that 'aside from backbone initialization, all training and evaluation settings were identical', but the four conditions differ in pretraining data, pretraining objective, and parameter count: ResNet50 uses ImageNet-1K supervised pretraining, ConvNeXtV2 uses masked autoencoding, MambaVision uses its own hybrid training scheme, and DINOv3 is a larger self-supervised ViT pretrained on a very large corpus. The observed performance gaps could therefore be driven by pretraining quality or capacity rather than by the backbone family. This is load-bearing for the conclusion that ConvNeXtV2 is 'well-matched' to mammography; the paper should either add experiments that disentangle architecture from pretraining (e.g., same pretraining objective/data across families, or pretrained-from-scratch controls) or explicitly restrict the conclusion to the ranking of these off-the-shelf backbones.
[Table 1 and Appendix C] All metrics in Table 1 are reported as point estimates from what appears to be a single run per condition, with no confidence intervals, standard deviations, or significance tests. Differences such as SGM1k AUC of 86.62 for ResNet50 versus 84.74 for MambaVision, or OPTIMAM AUC of 96.92 versus 97.35, may be within run-to-run variability, especially without stated seeds or repeats. The claim that 'across both datasets, modern backbones consistently outperformed older ResNet-style features' needs at least a small number of seeds per configuration and an appropriate paired test to be supported.
[Appendix C and Table 2] The detection branch is trained with only 3 object queries and a training cap of at most 3 target objects per image, yet Table 2 reports that OPTIMAM images contain up to 16 bounding boxes. This mismatch means the model cannot propose more than 3 candidates, and the localization evaluation ignores a substantial portion of annotated lesions on OPTIMAM. This does not necessarily change the relative ordering of backbones, but it directly affects the interpretation of localization metrics and the claim that the framework returns useful candidate regions; the authors should justify the cap or ablate it.
[Abstract and Section 3] No external comparison is made against existing mammography classification or lesion-localization models, so the title's phrase 'improve multi-task DETR' is only an internal statement relative to the worst backbone in this study. The absence of a baseline makes it difficult to assess whether the reported absolute values (e.g., 25.08% mAP@.5 on OPTIMAM) represent meaningful progress for clinical decision support. I recommend adding at least one prior mammography baseline or explicitly rephrasing the contribution as a backbone-ranking study.

minor comments (5)

[Table 1] Several numeric entries are run together in the table (e.g., '91.9476.37', '77.8484.25', '90.7081.40'), which appears to be a formatting error; the table needs clear column separation.
[Appendix A.5 and Appendix C] There is an internal inconsistency in the loss specification: Appendix A.5 defines the classification loss as standard cross-entropy, while Appendix C states that image-level classification is optimized with focal loss. The paper should state which loss is actually used, since this affects reproducibility.
[Appendix C] The experimental section does not report the number of random seeds, the early-stopping criterion beyond patience, or the checkpoint-selection rule, all of which are needed for exact reproduction of the reported metrics.
[Appendix D] Figure 2 is mentioned only in the appendix and is not referenced from the main text; adding a brief reference in Section 3 would make the qualitative evidence easier to interpret.
[Appendix B] The SGM1k dataset statistics are described only in aggregate; it would be helpful to state the number of malignant versus benign cases at the patient level, since the image-level counts may over-represent patients with multiple images.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the backbone comparison is a direct empirical benchmark on fixed patient-level test splits.

full rationale

The paper's central claim is an empirical ranking of four off-the-shelf backbones inside a fixed multi-task DETR framework. The numbers in Table 1 are measured on held-out test images, and no equation in the paper derives a result from its own target. The loss formulation in Appendix A.5 combines standard cross-entropy, L1/GIoU box losses, and objectness supervision, none of which is fitted to the reported AUC or mAP values. The only self-citation, Kha et al. 2024, appears in the introduction as background for multi-task learning and as the source of the SGM1k cohort; it is not used to justify the numerical outcomes or to forbid alternative backbones. Appendix C's statement that 'aside from backbone initialization, all training and evaluation settings were identical' describes the controlled downstream pipeline rather than a circular reduction; the fact that pretraining data and objectives differ across backbones is a genuine external-validity limitation (architecture and pretraining are entangled), but it is a correctness concern, not a circularity concern. No fitted input is renamed as a prediction, and no uniqueness or ansatz is imported from the authors' prior work. The derivation chain, such as it is, is self-contained: a fixed architecture, standard losses, public and described datasets, and directly measured test metrics. Therefore no circular step is present.

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

The paper introduces no new entities or theoretical constructs. The central claim depends on the quality of the annotations, the transferability of pretrained backbones, and the fairness of the fixed experimental protocol.

free parameters (3)

Number of object queries and training target cap = 3
Hand-chosen; with OPTIMAM containing up to 16 annotated boxes per image, capping at 3 may suppress detection metrics across all backbones.
Loss weights (lambda_bbox, lambda_GIoU, lambda_obj) = 5.0, 2.0, 1.0
Fixed by hand and equal across experiments; not fitted to data.
Input image size = 512x512
Uniformly resized; this may affect detection of small lesions but is constant across backbones.

assumptions (4)

domain assumption Bounding-box annotations in OPTIMAM and SGM1k are clinically accurate and consistently labeled.
The evaluation of localization depends entirely on the quality of these boxes; no annotation review is described.
domain assumption Pretrained backbone weights transfer to mammography and differences in pretraining do not dominate the comparison.
The paper varies only backbone initialization (Appendix C) and assumes the resulting ranking reflects architecture quality.
domain assumption The described patient-level split is correctly implemented without leakage.
Prevention of leakage is stated but not verified; no patient IDs or split code are provided.
domain assumption Joint multi-task training with a simple additive loss does not distort either task relative to single-task models.
No single-task baselines are reported, so the benefit of the multi-task setup itself is assumed.

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Cite this review

Pith. "Pith review of Modern Backbones Improve Multi-task DETR for Mammography Classification and Lesion Localization." pith.science (2026). https://pith.science/paper/72C4NE4U

@misc{pith2026260809801,
  author       = {Pith},
  title        = {Pith review of: Modern Backbones Improve Multi-task DETR for Mammography Classification and Lesion Localization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/72C4NE4U}},
  note         = {Machine review of arXiv:2608.09801}
}

read the original abstract

Joint exam-level prediction and candidate-region localization may improve the usefulness of AI support in mammography. We study this setting using a multi-task DETR framework, where shared representations support both image-level malignancy prediction and lesion localization, and evaluate its performance on OPTIMAM and a biopsy-confirmed SGM1k cohort. Across both datasets, modern backbones consistently outperformed older ResNet-style features, with ConvNeXtV2 and DINOv3 giving the strongest overall results, whereas MambaVision was less competitive. On OPTIMAM, ConvNeXtV2 achieved the best overall performance, reaching 97.96% AUC, 99.89% sensitivity, 25.08% mAP@.5, and 74.38% recall@.25. On SGM1k, DINOv3 gave the strongest overall results, with 90.97% AUC, 86.28% sensitivity, 82.00% specificity, 27.04% mAP@.5, and 77.32% recall@.25. These findings suggest that backbone quality is a critical factor in effective multi-task mammography, with ConvNeXtV2 emerging as a particularly strong and well-matched CNN backbone for mammography in this framework.

Figures

Figures reproduced from arXiv: 2608.09801 by the authors.

**Figure 2.** Qualitative comparison of Grad-CAM maps across backbones [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

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Lamport, Leslie , title =. 1986 , isbn =

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Deformable detr: Deformable transformers for end-to-end object detection , author=. arXiv preprint arXiv:2010.04159 , year=

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First conference on language modeling , year=

Mamba: Linear-time sequence modeling with selective state spaces , author=. First conference on language modeling , year=

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