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V. Nguyen

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Conference Jul 2026

GroundMed-SAM: Prompt-based Zero-shot Medical Image Segmentation

Medical image segmentation is a key component of computer-aided diagnosis and treatment planning. Despite substantial progress in deep learning–based models, most existing approaches depend heavily on large annotated datasets and often fail to generalize across heterogeneous clinical environments, limiting their deployment in real-world settings characterized by domain shifts and scarce expert annotations. This paper presents a zero-shot learning framework named GroundMed-SAM for medical image segmentation. The framework integrates GroundingDINO for prompt-based region localization and MedSAM for mask generation. To address the weak alignment between visual features and medical semantics in GroundingDINO, which is pretrained on general domain image-text pairs, we introduce learnable medical text embeddings that explicitly parameterize domain-specific terminology in a continuous semantic space. These embeddings are optimized during training to better align medical concepts with visual representations, thereby strengthening text-image correspondence and improving detection-guided segmentation. The proposed framework preserves true zero-shot capability, enabling segmentation of previously unseen anatomical structures without task-specific labels. Extensive experiments on multiple public datasets across diverse modalities and clinical contexts demonstrate that our method achieves competitive segmentation performance in-domain while exhibiting superior robustness under cross-domain evaluation. Although supervised baselines outperform the proposed framework by only 3–5% on in-domain datasets, they experience substantial performance degradation when evaluated on unseen domains. Additionally, the framework achieves an AUC of 98.9 in endoscopic polyp detection, highlighting the effectiveness of the proposed medical-aware textual embeddings in guiding region localization. These results demonstrate the effectiveness of the proposed framework in improving cross-domain generalization for medical image segmentation with limited annotations.

V. Nguyen, Hoang Quan Luong, Phuc Ngoc Pham · 0 citations
Conference Jul 2026

Improving GI Polyp Segmentation with a Balanced-Mix-Driven

Gastrointestinal (GI) endoscopy is a cornerstone diagnostic procedure for detecting inflammatory diseases, polyps, and early-stage cancers. Recent advances in deep learning have significantly improved automated endoscopic image analysis; however, their performance remains limited by scarce annotations, severe class imbalance, and poor generalization across diverse imaging conditions. Moreover, jointly learning classification and segmentation poses additional challenges due to task imbalance and the high annotation cost of pixel-level labels. To address these limitations, we propose a Balanced-Mix-Driven framework that leverages 99,417 unlabeled images from the HyperKvasir dataset through Self-Supervised Learning (SSL)-based pretraining. Our core contribution, Balanced-Mix, is an interpolation strategy that progressively shifts from coarse to fine-grained mixing during pretraining, preventing trivial representation learning. Experimental results on the Kvasir-SEG dataset demonstrate that our method achieves a Dice score of $\mathbf{9 2 . 4 0} \boldsymbol{\%}$ and an mIoU of $\mathbf{8 7 . 0 1 \%}$, outperforming established baselines such as UNet++ and ResUNet++. This validates the effectiveness of curriculum-based self-supervised learning for dense medical prediction tasks.

V. Nguyen, Nhat Duong Nguyen Nguyen, Dung Nguyen Van Tuan et al. · 0 citations