From 2D to 3D Without Extra Baggage: Data-Efficient Cancer Detection in Digital Breast Tomosynthesis
To address the underutilization of 3D volumetric information, scarcity of annotated data, and high model complexity in digital breast tomosynthesis (DBT), this paper proposes M&M-3D—a parameter-efficient architecture enabling zero-shot incremental transfer from 2D pre-trained weights (e.g., FFDM) to DBT. Without increasing model parameters, it introduces a learnable 3D reasoning mechanism that fuses malignancy-guided voxel-level features with slice-level responses. Its core innovation is a lightweight, feature-mixing-based 3D contextual modeling approach—avoiding explicit 3D convolutions or auxiliary supervision. On the BCS-DBT benchmark, M&M-3D achieves a 4% improvement in classification accuracy and a 10% gain in lesion localization performance. Under low-data regimes, it outperforms complex 3D models by 20–47% in localization and 2–10% in classification, significantly enhancing sample efficiency and generalizability for small-sample DBT cancer detection.