Temporal Prior Attention in Breast Lesion Segmentation

• Temporal Prior Attention (TPA) is an attention mechanism that dynamically fuses features from consecutive imaging scans to enhance lesion segmentation.
• TPA integrates into dual-encoder networks alongside BI-RADS Consistency Regularization to robustly weigh clinically relevant changes across timepoints.
• Empirical results demonstrate a 5% increase in Dice coefficient, highlighting TPA's practical impact on improving temporal feature fusion in medical diagnostics.

Temporal Prior Attention (TPA) is an architectural module designed to facilitate dynamic, context-aware integration of information from temporally-ordered medical imaging data—most notably in breast lesion segmentation tasks where longitudinal scans are available. TPA addresses the core challenge of leveraging clinical context across timepoints to enable reliable detection and localization of subtle, emerging pathology, particularly in high-risk screening scenarios. This attention mechanism is often situated alongside auxiliary regularizers that encode domain knowledge, such as BI-RADS Consistency Regularization, to synergistically improve model sensitivity to pathologically meaningful changes in imaging phenotype (Kamran et al., 1 Aug 2025).

1. Motivation and Problem Setting

Temporal analysis is fundamental in medical diagnostics, particularly in the interpretation of dynamic studies such as Breast Dynamic Contrast-Enhanced MRI (DCE-MRI). Radiologists frequently compare imaging findings across multiple visits, considering both subtle temporal evolution and contextual clinical assessments (e.g., BI-RADS scores) to discern emerging lesions and disease progression. However, early deep learning segmentation approaches for DCE-MRI primarily relied on single-timepoint data, thereby neglecting the temporal dimension critical for accurate detection of small or subtle anomalies. There is a need for modeling approaches that can not only process longitudinal image pairs but also selectively incorporate prior information based on its diagnostic relevance.

2. Temporal Prior Attention: Mechanism and Formulation

The Temporal Prior Attention (TPA) block is engineered to dynamically weigh the contribution of previous and current scans in a paired input configuration. Given two temporally-ordered 3D volumes—typically a current scan $x_t$ and its immediate predecessor $x_{t-1}$—the TPA block operates within the skip connections of a dual-encoder network (with shared weights) (Kamran et al., 1 Aug 2025).

• Attention Weight Generator: At a specified encoder stage, feature maps $k_t^{(m)}$ and $k_{t-1}^{(m)}$ are processed in parallel. The TPA block computes spatially- or channel-wise attention weights to modulate the influence of each timepoint’s feature representation on the downstream decoder.
• Feature Modulation: These weights are used to produce a selective fusion, enabling the network to upweight or suppress past scan features based on their relevance—an implicit robustness to temporal misalignment or clinically uninformative priors.
• Differentiability and Integration: The attention mechanism is fully differentiable and is integrated into the network’s skip-connections, allowing end-to-end learning of how historical information should condition the current inference.

This separation of dynamic (forward-pass) attention weighting from loss-based priors (such as BI-RADS Consistency Regularization) ensures conceptual modularity—TPA directly affects feature propagation while regularizers shape the learned representations indirectly through the training objective.

3. Network Architecture and TPA Block Placement

The LesiOnTime framework, which introduced TPA for lesion segmentation, employs a 3D PlainConvUNet backbone with six encoder stages (channels: [32, 64, 128, 256, 320, 320]) and a mirrored decoder (Kamran et al., 1 Aug 2025). The dual-encoder design processes $x_{t-1}$ and $x_t$ in parallel, with shared weights ensuring consistent feature abstraction.

The TPA block is inserted in each encoder-decoder skip connection. At each skip, feature maps from both timepoints are combined via the attention mechanism before being concatenated with the corresponding decoder features. This placement allows multi-scale, hierarchical temporal feature fusion and ensures that temporal priors are incorporated at various levels of semantic abstraction.

Component	Role in LesiOnTime	Operational Phase
Dual encoder	Processes $x_t$, $x_{t-1}$	Forward pass
TPA block	Attends/fuses features	Forward pass
BCR regularizer	Aligns latent spaces (BIRADS)	Training only

This design decouples temporal attention (TPA) from clinical regularization (BCR), ensuring that temporal cues are modulated independently from BI-RADS-driven constraints.

4. Training Strategy and Data Pairing

LesiOnTime processes each sample as a pair of scans—$(x_{t-1}, x_t)$—corresponding to a patient’s two most recent visits. BI-RADS scores, treated as ordinal integers (0—6), provide a clinical reference for both supervision and regularization. No binarization of BI-RADS or grouping beyond immediate predecessor pairing is employed. The TPA module operates identically for all pairs, independent of the magnitude or direction of BI-RADS change, providing a generalizable mechanism for encoding temporal continuity or divergence.

All convolutions are 3×3×3, with InstanceNorm and LeakyReLU. No dropout layers are used. Training data comprises high-risk screening DCE-MRI time series, with the time interval between scans constrained to 6–24 months to match clinical usage patterns (Kamran et al., 1 Aug 2025).

5. Empirical Impact and Ablation

Evaluated on a curated longitudinal dataset of high-risk breast DCE-MRI, LesiOnTime outperforms state-of-the-art single-timepoint and longitudinal baselines by 5% in Dice coefficient (Kamran et al., 1 Aug 2025). Ablation studies reveal that both TPA and BI-RADS Consistency Regularization provide complementary performance gains—removing TPA reduces the model’s capacity to leverage temporal continuity, particularly for small lesion detection. These results underscore the importance of TPA in facilitating clinically meaningful temporal integration, especially in scenarios where registration error or variable-quality prior scans would otherwise degrade model reliability.

6. Limitations and Practical Considerations

A noted limitation is the usage of single-center data, with no external longitudinal validation available at the time of publication (Kamran et al., 1 Aug 2025). TPA’s benefit may be modulated by the accuracy of temporal registration; mismatched or non-informative previous scans may attenuate performance unless the attention weights adaptively downweight such input. TPA introduces minimal computational overhead relative to the rest of the network and requires no additional annotation beyond what is needed for paired training.

A plausible implication is that TPA is especially advantageous when temporal context is valid and clinically relevant, but its utility may decrease if applied to domains with heterogeneous scan intervals or widespread unregistered acquisitions.

7. Relation to Consistency Regularization and Broader Context

While TPA is orthogonal in function to consistency regularization strategies such as BI-RADS Consistency Regularization (BCR), their joint deployment in LesiOnTime demonstrates that attention-based temporal fusion and explicit clinical priors each address distinct axes of robust, generalizable learning. TPA enables flexible, input-adaptive modeling of change trajectories, while BCR encodes clinical expectations directly into the latent space. Together, these approaches contribute to improved early lesion segmentation under the constraints of real-world longitudinal screening (Kamran et al., 1 Aug 2025).

TPA and its analogs represent a growing trend in medical imaging: the harmonization of temporal context and clinical meta-data within unified deep learning frameworks, bridging the gap between algorithmic design and diagnostic reasoning.