ResearchResearch paperComputer Vision · Efficiency & Inference · Interpretability1 source · Oct 14, 2026

GDBA-Net: A Gated Dual-Branch Adaptation of SAM2 for Robust Cross-Domain Nuclei Segmentation

We introduce GDBA-Net, a parameter-efficient nuclei segmentation framework built around the proposed Gated Dual-Branch Adapter (GDBA).

Accurate nuclei segmentation underpins quantitative computational pathology, yet remains challenging due to morphological variability, weak boundaries, nuclear crowding, and pronounced domain shifts arising from differences in tissue preparation, staining, and acquisition.
While deep learning and recent foundation models have improved segmentation performance under matched distributions with the original training data, their generalization across heterogeneous histopathology domains remains limited.
GDBA-Net adapts a frozen SAM2 encoder by integrating pretrained semantic representations with explicit frequency-derived structural priors.
Despite using only 4.3 million trainable parameters, GDBA-Net achieves state-of-the-art in-dataset performance and consistently superior cross-dataset generalization across six diverse nuclei segmentation benchmarks under fully supervised, cross-dataset, and five-shot adaptation protocols, outperforming competing CNN-, transformer-, and foundation model based methods.

[1]GDBA-Net: A Gated Dual-Branch Adaptation of SAM2 for Robust Cross-Domain Nuclei Segmentation
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 12:12 PM
We introduce GDBA-Net, a parameter-efficient nuclei segmentation framework built around the proposed Gated Dual-Branch Adapter (GDBA).
Accurate nuclei segmentation underpins quantitative computational pathology, yet remains challenging due to morphological variability, weak boundaries, nuclear crowding, and pronounced domain shifts arising from differences in tissue preparation, staining, and acquisition.

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