MammoMix: Leveraging Mixture of Experts for Robust Mammogram Breast Detection
Breast lesion detection in mammography remains a challenging task due to variations in image quality, lesion appearance, and population demographics across datasets. While current object detectors such as YOLO and DETR achieve strong results on individual datasets, their performance often degrades when trained on or applied across heterogeneous sources. To address this, we propose MammoMix, a novel framework based on Mixture-of-Experts (MoE) paradigm for robust and generalizable lesion detection. In MammoMix, each expert model is trained on a specific domain, allowing it to specialize in distinct characteristics of its source data. A gating mechanism adaptively weighs contributions from e...
| Authors | Dinh Tan Nguyen, Hoang Quan Dang, Chen Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 11 August 2026 |
| Last updated at source | 11 August 2026 |
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