Title: Dual-Res Tandem Mamba-3D: Bilateral Breast Lesion Detection and Classification on Non-contrast Chest CT
Venue: NeurIPS 2025
Accepted: yes
Keywords: Opportunistic Bilateral Breast Cancer Analysis

Non-contrast Chest CT

Joint Detection and Classification

State Space Models
Abstract: Breast cancer remains a leading cause of death among women, with early detection significantly improving prognosis. Non-contrast computed tomography (NCCT) scans of the chest, routinely acquired for thoracic assessments, often capture the breast region incidentally, presenting an underexplored opportunity for opportunistic breast lesion detection without additional imaging cost or radiation. However, the subtle appearance of lesions in NCCT and the difficulty of jointly modeling lesion detection and malignancy classification pose unique challenges.
In this work, we propose Dual-Res Tandem Mamba-3D (DRT-M3D), a novel multitask framework for opportunistic breast cancer analysis on NCCT scans. DRT-M3D introduces a dual-resolution architecture, which captures fine-grained spatial details for segmentation-based lesion detection and global contextual features for breast-level cancer classification. It further incorporates a tandem input mechanism that models bilateral breast regions jointly through Mamba-3D blocks, enabling cross-breast feature interaction by leveraging subtle asymmetries between the two sides. 
Our approach achieves state-of-the-art performance in both tasks across multi-institutional NCCT datasets spanning four medical centers. Extensive experiments and ablation studies validate the effectiveness of each key component.


Peer reviews:
comments: SUMMARY:
This paper introduces DRT-M3D, a multitask, dual-resolution network utilizing Mamba-3D blocks to perform both segmentation-based lesion detection and malignancy classification on non-contrast chest CT (NCCT) scans. The authors provide comprehensive results for a methodology that can be considered a great contribution. However, the text is complex and should be simplified for all readers.

STRENGTHS AND WEAKNESSES:
Strengths:
- This manuscript explores NCCT, which is underutilized for breast cancer detection. This is a novel and clinically relevant use case.
- I think this methodology with comprehensive experiments presented in this paper can be considered novel.
- This manuscript outperforms several SOTA methods across multiple internal and external datasets.

Weaknesses:
- This manuscript is complex to follow. Some sections are too math-heavy without sufficient intuition.
- Figures should also be simplified. I suggest adding a figure to introduce the framework in a simple way.
- The text should be double-checked to fix some mistakes. For example, Figure 1 is not referenced or explained in the text. All figures should be explained in detail.

QUESTIONS:
I mentioned my comments in the weakness section. Please address those comments.

LIMITATIONS:
NCCT is not optimized for breast imaging (unlike mammography or DCE-MRI), and lesions may be less visible or ambiguous without contrast.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposed Dual-Res Tandem Mamba-3D (DRT-M3D), a 3D mamba model for breast cancer detection from non-contrast CT data. It take the efficiency advantage of the Mamba model to process very long 3D voxel sequence and make use of the multi-view relationship of the breast to improve the detection. The proposed method is evaluated on 4 datasets and outperforms existing baselines with a notable gap according to the detailed evaluation.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n\n1. The proposed method was designed for a task where high resolution 3D voxel segmentation is needed, which properly validated the reason of using Mamba model. Different regular 2D cases, the proposed method is often operating on sequence over 10k tokens (according to the paper). The experimental results also confirmed the computational efficiency of the proposed model under the given settings.\n2. The proposed method seems to be the very first attempt to make use the multi-view relationship of the breast for the sick of breast cancer detection in NCCT. \n3. The experimental evaluation is very convincing and the paper is well-written and overall easy to follow. The evaluation has demonstrates the advantage of the proposed method under various settings, and the proposed method seems to be the best in general.\n\nWeakness:\n\n1. The proposed method has a much better computational efficiency (FLOPs) comparing with regular transformer-based method. However, its computational speed seems to be much slower than the baselines according to the Figure 9 in the supplement even during the inference.\n2. In section 3.3, the input from 2 views are placed as a pseudo-3D volume and then flattened before feeding to the mamba module. However, the reviewer didn’t see the reason of doing this. When the multi-view input was arranged as it is in Figure 3, the output sequence should be the same as simply concatenate them separately, regardless to the traverse order in 3D space. Since all the padded tokens are skipped, and there is no positional embedding, it seems to be an unnecessary operation to the reviewer. Some more clarification will help better understand this.\n3. While applying multi-view information in 3D NCCT breast cancer detection is a new setting, the idea of feeding multi-view inputs to an attention-style model to obtain a cross-view relationship is not that novel. For instance, [A] also feed the multi-view breast mammography to the transformer model with cross-attention to learn cross-view information. Still, this is a minor weakness, and the reviewer is still very glad to see this work.\n4. Another small issue is about the fairness of the evaluation of the main results. The proposed method was first pre-trained with mask reconstruction task for a long time and then fine-tuned for detection task. However, it seems that the baselines (except ViT baseline) are not pre-trained like this. Figure 5 suggests that pre-training is very important for the final results. So, it is unclear if the performance improvement is mainly from the pre-training, or better design.\n\n[Reference]\n\n- [A] Sun, Zizhao, et al. \"Transformer based multi-view network for mammographic image classification.\" *International conference on medical image computing and computer-assisted intervention*. Cham: Springer Nature Switzerland, 2022.\n\nQUESTIONS:\nThe reviewer wonders how is the training speed of the proposed method comparing with other baselines, will it have a much longer training time comparing with other methods?\n\nLIMITATIONS:\nLimitation was discussed in the paper.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes Dual-Res Tandem Mamba-3D (DRT-M3D), a multitask framework for bilateral breast lesion detection and malignancy classification using non-contrast chest CT NCCT. The approach leverages a dual-resolution architecture to balance fine-grained segmentation details and global contextual features, combined with a tandem input mechanism that models bilateral breast regions jointly via Mamba-3D blocks. This design enables cross-breast feature interaction to exploit subtle asymmetries. Experiments on multi-institutional NCCT datasets demonstrate state-of-the-art performance in both tasks, outperforming UNet, SwinUNETR, and Mamba-based baselines. Key contributions include the unified multi-task framework, dual-resolution design with mutual fusion, tandem bilateral modeling, and validation on diverse clinical data.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1、The dual-resolution design and tandem input mechanism for bilateral modeling are innovative, addressing the challenge of integrating fine-grained detection with global classification while leveraging breast asymmetry.\n2、Experiments on three internal and one external dataset, including ablation studies on key components (dual-resolution, mutual fusion, tandem input), validate the approach’s effectiveness. The focus on opportunistic analysis of NCCT avoids additional radiation/cost, aligning with practical clinical needs.\n\nWeaknesses:\n1.\tIn the Pre-Stage of the paper (Sec. 3.1), the pre-trained segmentation model is used to crop the breast region. If the small lesions are lost in the pruning process, it may lead to the failure of subsequent detection. Non-contrast CT itself has low resolution, and the contrast of small lesions (such as microcalcifications) is insufficient, which may further increase the risk of missed detection.\n2.\tThe article mentions the use of Delta visualization to compare DRT-M3D with DR-M3D models and highlights the advantages of the two-input mechanism. However, from the information provided, it seems that these visualizations mainly focus on the single-layer N=1 case, but it may also ignore the real behavior of the model under the multi-layer structure. Although FIG. 11 demonstrates the significant Delta response on the LR path, a comparative display for the HR path or other key features is inadequate. This can make it difficult for the reader to fully understand all the differences between the two models and their practical significance.\n3.\tThe paper demonstrates that applying the tandem input concept to ViTs and CNNs (Appendix E, Table 10) is less effective. While this is a good result for the authors, the analysis could go deeper. The paper attributes ViT's failure to the excessive number of tokens from small patches. Is this purely a memory/computation issue, or a fundamental limitation in the attention mechanism's ability to handle such granularity? A more nuanced discussion on why Mamba's selective state-space mechanism is uniquely suited for this \"tandem\" sequence concatenation, beyond linear complexity, would elevate the paper's technical contribution.\n\nQUESTIONS:\n1.\tHow will the authors expand data collection to include cross-regional populations, and have preliminary results from diverse cohorts been tested?\n2.\tIt is mentioned in the paper that the method relies on a pre-trained segmentation model (Pre-Stage) to locate the breast region. What is the impact on Main-Stage performance if this stage fails (for example, if the breast boundary is blurred in obese patients)?\n3.\tAre there specific metrics to validate detection of tiny abnormalities, which are critical for early diagnosis?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a dual-resolution 3D Mamba framework for breast lesion detection and classification from early breast CT images. The authors propose a multi-task learning framework that combines pixel-level lesion segmentation with image-level diagnosis tasks, effectively integrating both local details and global features. Experiments conducted on internal and external datasets demonstrate the state-of-the-art performance of the proposed method.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. This paper presents a novel Mamba-based framework to address the important problem of early breast lesion detection. The design of the tandem input, which incorporates both left and right breast images, is particularly innovative and interesting.\n2. The authors conduct comprehensive experiments and present solid results across three datasets.\n3. The paper is well-organized, and the quality of the English writing is commendable.\n\nWeaknesses: \n\n1. Missing comparsion with SOTAs: The authors did not compare with recent SOTA such as [1]. \n\n[1] Sun J, Xi X, Wang M, et al. A deep learning model based on chest CT to predict benign and malignant breast masses and axillary lymph node metastasis[J]. Biomolecules and Biomedicine, 2025.\n\n2. Limited Novelty & Relations between task and architecture design: the author designed a framework specific for breast lesion detection and classification. The novelty of their framework seem only two fold: (1) using Mamba on breast lesion detection and classification (2) design a multi-task learning (classification and detection) strategy to obtain local & global features. However, regarding the first point, the author fails to thoroughly discuss the relation between mamba and this specific task, why should we use Mamba instead of other backbones? Is there any special characteristic of non-contrastive CT that mamba could excel at and outperform other backbones?\nRegarding the second point, I think there are plenty of previous works that has design such multi-task learning strategy [2,3], just on different modalities or different lesions, they also design their multi-task learning framework for the same purpose.\n\n[2] He Q, Yang Q, Su H, et al. Multi-task learning for segmentation and classification of breast tumors from ultrasound images[J]. Computers in Biology and Medicine, 2024, 173: 108319.\n[3] Xu, Mengjuan et al. “A Regional-Attentive Multi-Task Learning Framework for Breast Ultrasound Image Segmentation and Classification.” IEEE Access 11 (2023): 5377-5392.\n\n3. “Tandem input” is confusing: the definition of tandem input is a little confusing, is it just combine the feature sequence of left and right beast together as one unified sequence? I think the author should provide some references to give a better explanation of “tandem input”.\n\n4. Missing comparison with contrast CT: I believe including comparison with using contrast CT as upper-bound could better reveal the clinical value of the proposed method.\n\n5. Limited performance improvement: from Table.1, we can see that there is a very limited performance improvement comparing the proposed method and other very common baseline models, the author also didn’t compare methods that are specially designed for breast lesion classification.\n\n6. Unfair comparison: the author conduct a large-scale self-supervised pre-training for their proposed method before finetuning, however, for their counterparts which are majorly from computer vision field, they did not employ the pretraining, which resulted in unfair comparison.\n\n7. Unclear comparison in the visualization: In figure. 4, the authors present qualitative comparison results between their methods and other counterparts, however, the difference is too small and unclear. I think a zooming in and a highlight on the error part will do better.\n\n8. Model Efficacy: since the topic is early detection, I believe detection speed and efficacy are both important, the author should add training time/inference time, and memory consumption.\n\nQUESTIONS:\n1. The authors should compare their method with more recent state-of-the-art (SOTA) approaches for tumor classification based on segmentation methods, including those mentioned in the weaknesses section.\n\nFor example, A Data-Efficient Pan-Tumor Foundation Model for Oncology CT Interpretation, https://arxiv.org/abs/2502.06171\nFreeTumor: Large-Scale Generative Tumor Synthesis in Computed Tomography Images for Improving Tumor Recognition https://arxiv.org/abs/2502.18519\n\n2. The novelty of the proposed method should be further emphasized. What are the unique aspects and motivations behind this approach compared to other Mamba-based methods and related techniques?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-05"}