A team of researchers from Qatar University and the University of Hyogo has now taken a direct swing at that problem. In a study published in Multimedia Tools and Applications, they introduce TomoMamba, a two-stage deep learning framework that, according to the authors, is the first application of selective state-space models to inter-slice feature propagation in DBT. The work arrives at a moment when the Mamba architecture, a relative newcomer to the deep learning scene, has been making waves for its ability to model long sequences with linear computational cost, in contrast to the quadratic cost of transformer attention.

The framework’s design mirrors the clinical workflow of breast screening. Stage 1 acts as a high-throughput triage system: a dual-branch 2.5D convolutional network processes the craniocaudal and mediolateral oblique views of each breast through a shared ResNet-18 backbone, aggregates information across slices with a learnable attention mechanism, and fuses the two view-level representations into a single verdict of normal or suspicious. On the validation partition of the public BCS-DBT dataset, this screening stage achieved a breast-level area under the receiver operating characteristic curve of 0.97, correctly filtering out 94.5 percent of normal breasts while identifying 36 of 41 suspicious ones.

Only breasts flagged as suspicious proceed to Stage 2, where the real architectural novelty lives. A ResNet-18 backbone with a lightweight feature pyramid network extracts features from each of the fifteen representative slices retained per volume. Then, at every spatial position in the feature map, the model extracts a fifteen-element sequence of slice-wise features and feeds it through a Mamba selective state-space model. The crucial distinction from earlier recurrent approaches is that Mamba’s state-transition matrices are input-dependent: the amount each slice contributes to the running hidden state varies with the content of that slice. A lesion that activates strongly on two or three adjacent slices therefore leaves a depth summary that carries information about where along the depth axis the signal appeared, something slice-independent methods discard entirely and full 3D convolutions capture only at substantial memory cost.

The Mamba output is folded back into the per-slice features through a gated residual connection, a sigmoid gate that makes per-channel keep-or-suppress decisions conditioned on the depth context. Introspection across 72 validation volumes showed the gate is channel-selective rather than slice-selective: at lesion positions a mean of 40 of 128 channels fell outside the neutral band, against 23.5 at background positions, a contrast of 1.70 times with a large effect size. Downstream, an anchor-free CenterNet-style detector predicts heatmaps, box sizes and offsets directly from the Mamba-enhanced features, and classification is performed on small feature patches cropped at the detected lesion centers rather than pooled globally. That cropping choice proved essential: a pooling-only baseline oscillated between all-cancer and all-benign predictions without ever converging on the tiny training set of 200 biopsied volumes from just 101 patients.

The evaluation rests on BCS-DBT, hosted on The Cancer Imaging Archive and collected at Duke University Hospital, which remains the only public DBT dataset combining biopsy-confirmed pathology labels with lesion-level bounding-box annotations. The class distribution is extreme, with normal cases outnumbering cancers by more than 100 to 1, which is precisely why the authors split the problem in two. On the validation partition, Stage 2 attained a patient-level AUC of 0.80 with a lesion detection rate of 72.0 percent. On the untouched test partition, however, classification fell to 0.62, an interval that spans chance, while detection degraded only modestly to 62.5 percent. The authors are unusually candid about this: they present lesion localization, not benign-versus-cancer discrimination, as Stage 2’s demonstrated contribution.

Chained together, the two stages reached a breast-level AUC of 0.93 on validation and 0.92 on the held-out test set, forwarding just 14 percent of breasts for expensive diagnostic analysis while losing two of 20 cancers at screening. The authors warn that this cascade figure reflects the screening stage’s normal-versus-suspicious separation rather than genuine cancer discrimination, and that roughly one cancer in ten would be missed if the cascade were used as a standalone filter. They position the system as a reader-support tool to be used alongside, not in place of, radiologist interpretation.

The controlled baseline comparisons are where the architectural argument gets its sharpest test. Under identical data, preprocessing and hyperparameters, TomoMamba was pitted against a bidirectional GRU, a transformer variant and a slice-independent baseline, all matched to within one percent on parameter count. On the validation partition, the cross-slice architectures were not separable by classification AUC, and the authors decline to rank them on that metric. But at matched false-positive rates, TomoMamba localized more lesions than every alternative on validation, and on the test partition it achieved the highest patient-level AUC and the lowest Brier score of the five configurations. Most tellingly, removing the gated cross-slice residual dropped patient-level AUC from 0.798 to 0.548, the only architectural ablation that reached statistical significance at 0.009.

The team also confronted a subtle methodological hazard head-on. Because radiologist annotations guided which slices were retained during preprocessing, the model could in principle have exploited the annotated slice’s position as a hidden marker. The authors tested this directly: detection rates showed no significant difference depending on where the annotated slice sat in the sequence, and under a fully annotation-independent selection procedure, patient-level classification changed little, falling from 0.798 to 0.748 on validation. Since annotations do not exist at deployment, they treat the annotation-independent figures as the deployment-relevant results. Failure analysis added further nuance: the dominant failure mode was classification rather than localization, with most misclassifications clustered within 0.03 of the decision threshold, suggesting a calibration problem rather than a detection one.

The limitations are stated with rare honesty. The study is single-institution, single-scanner, and underpowered for benign-versus-cancer classification with only 101 biopsied training patients; no published DBT method has yet demonstrated cross-site transfer. The much-hyped efficiency of state-space models also did not materialize at this scale: at fifteen slices per volume, Mamba’s sequential scan was actually slower than both the recurrent and attention baselines, though its linear scaling should win out on deeper acquisitions. Still, the significance of the work lies in its disciplined template for evaluating new architectures on small clinical datasets, reporting all three official partitions, quantifying seed variability, and refusing to claim more than the data can support. As larger annotated DBT cohorts emerge, the question of whether selective state-space propagation becomes the backbone of volumetric cancer screening will finally get the data it deserves.

Subject of Research: Deep learning with selective state-space models for cross-slice analysis in digital breast tomosynthesis for breast cancer screening and diagnosis

Article Title: TomoMamba: a two-stage model with state-space cross-slice propagation for breast cancer diagnosis in digital breast tomosynthesis

Article References: Soliman, S., Zafari, Y., Rashed, E. A., & Mabrok, M. (2026). TomoMamba: a two-stage model with state-space cross-slice propagation for breast cancer diagnosis in digital breast tomosynthesis. Multimedia Tools and Applications, 85(10), Article 776. https://doi.org/10.1007/s11042-026-21943-x

Image Credits: AI Generated

DOI: 10.1007/s11042-026-21943-x

Keywords: digital breast tomosynthesis, breast cancer, Mamba, state-space models, deep learning, lesion detection, computer-aided diagnosis, BCS-DBT dataset, medical imaging AI, CenterNet, screening cascade, feature propagation