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From the 1 of 13 linked papers with an AI index.

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13 papers

cs.CV2026

Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography

Quoc-Huy Trinh, Minh-Van Nguyen, Ulas Bagci

The paper presents Rad-JEPA 3D, a self‑supervised joint‑embedding model that learns 3D CT representations by predicting latent features of a full scan from a masked view, using a h…

cs.CV2026

ESC: Emotional Self-Correction for Reliable Vision-Language Models

Tien-Huy Nguyen, Minh-Nhat Nguyen, Nguyen Nhat Huy +9

Vision-language models (VLMs) have achieved strong performance across diverse multimodal tasks, yet they remain vulnerable to unreliable reasoning. Existing self-correction methods…

cs.CV2026

Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI

Quang-Khai Bui-Tran, Minh-Toan Dinh, Thanh-Huy Nguyen +3

Accurate liver segmentation in multi-phase MRI is vital for liver fibrosis assessment, yet labeled data is often scarce and unevenly distributed across imaging modalities and vendo…

cs.CV2026

Robust White Blood Cell Classification with Stain-Normalized Decoupled Learning and Ensembling

Luu Le, Hoang-Loc Cao, Ha-Hieu Pham +2

White blood cell (WBC) classification is fundamental for hematology applications such as infection assessment, leukemia screening, and treatment monitoring. However, real-world WBC…

cs.CV2026

Scribble-Supervised Medical Image Segmentation with Dynamic Teacher Switching and Hierarchical Consistency

Thanh-Huy Nguyen, Hoang-Loc Cao, Dat T. Chung +5

Scribble-supervised methods have emerged to mitigate the prohibitive annotation burden in medical image segmentation. However, the inherent sparsity of these annotations introduces…

cs.CV2025

DuetMatch: Harmonizing Semi-Supervised Brain MRI Segmentation via Decoupled Branch Optimization

Thanh-Huy Nguyen, Hoang-Thien Nguyen, Vi Vu +6

The limited availability of annotated data in medical imaging makes semi-supervised learning increasingly appealing for its ability to learn from imperfect supervision. Recently, t…