1 citations · 2 across the 13 of their papers we have counts for
14 papers
CircuitGate: Logic-Consistent Circuit-Level Functional Modeling for And-Inverter Graphs
Qifan Zhang, Ruijie Li, Fangzhou Zhang +6
And-Inverter Graphs (AIGs) are fundamental representations for logic synthesis and verification in Electronic Design Automation (EDA). As structured representations of complex digi…
Asymmetric Paired-Annotation Learning for Multi-Structure ULF Pediatric Brain MRI Segmentation
Ha-Hieu Pham, Dang P. M. Cao, Minh Hoang Pham +4
Portable ultra-low-field (ULF) MRI can expand access to pediatric neuroimaging, but segmentation at 0.064 T remains challenging because anatomical boundaries are weakly delineated,…
Candidate-Expanding Routing with Permutation-Stabilized Experts for Mixed-Format Medical VQA
Hai-Dang Nguyen, Huy-Hieu Pham
Mixed-format medical visual question answering (VQA) requires stable option selection and machine-readable free-text output. The two formats fail differently: multiple-choice predi…
Entity-Constrained CBCT Retrieval for Low-Resource Dental Record Completion
Nhi Ngoc-Yen Nguyen, Thai Nguyen, Kiet Huynh Cao Tuan +1
Completing dental records from cone-beam computed tomography (CBCT) is difficult when annotation is scarce and individual clinical fields are supported by different types of eviden…
Who Gets Missed in the Tail? Thresholded Subgroup Underdiagnosis in Long-Tailed Chest X-ray Classification
Ha-Hieu Pham, Hai-Dang Nguyen, Dang P. M. Cao +5
In chest X-ray (CXR) classification, acceptable ranking performance can still leave rare-positive patients below threshold, especially within subgroups. We study this pre-deploymen…
Single-Stage Hierarchical Rectification for Weakly Supervised Histopathology Segmentation
Duc T. Nguyen, Hoang-Long Nguyen, Thanh-Ha DO +1
Existing weakly supervised semantic segmentation (WSSS) methods in computational pathology rely on a multi-stage paradigm: class activation map (CAM) generation, offline pseudo-mas…
MIC: Maximizing Informational Capacity in Adaptive Representations via Isotropic Subspace Alignment
Dang Nguyen Hong, Nhi Ngoc-Yen Nguyen, Huy-Hieu Pham
Although multi-scales representation learning enables elastic-dimension embeddings, nested subspaces often suffer from dimensional redundancy and spectral collapse. To address this…
EVL-ECG: Efficient ECG Interpretation With Multi-Aspect Heterogeneous Knowledge Distillation
Dang Nguyen Hong, Nhi Ngoc-Yen Nguyen, Huy-Hieu Pham
High-fidelity ECG interpretation is increasingly reliant on massive foundation models, yet their deployment in clinical edge-care remains hindered by extreme computational demands.…
CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification
Hexin Dong, Yi Lin, Pengyu Zhou +25
Chest X-ray (CXR) interpretation is hindered by the long-tailed distribution of pathologies and the open-world nature of clinical environments. Existing benchmarks often rely on cl…
Synergizing Deep Learning and Biological Heuristics for Extreme Long-Tail White Blood Cell Classification
Duc T. Nguyen, Hoang-Long Nguyen, Huy-Hieu Pham
Automated white blood cell (WBC) classification is essential for leukemia screening but remains challenged by extreme class imbalance, long-tail distributions, and domain shift, le…
Digital FAST: An AI-Driven Multimodal Framework for Rapid and Early Stroke Screening
Ngoc-Khai Hoang, Thi-Nhu-Mai Nguyen, Huy-Hieu Pham
Early identification of stroke symptoms is essential for enabling timely intervention and improving patient outcomes, particularly in prehospital settings. This study presents a fa…
CLEAR: Causal Learning Framework For Robust Histopathology Tumor Detection Under Out-Of-Distribution Shifts
Kieu-Anh Truong Thi, Huy-Hieu Pham, Duc-Trong Le
Domain shift in histopathology, often caused by differences in acquisition processes or data sources, poses a major challenge to the generalization ability of deep learning models.…
Graph-Theoretic Consistency for Robust and Topology-Aware Semi-Supervised Histopathology Segmentation
Ha-Hieu Pham, Minh Le, Han Huynh +2
Semi-supervised semantic segmentation (SSSS) is vital in computational pathology, where dense annotations are costly and limited. Existing methods often rely on pixel-level consist…
Learning Disentangled Stain and Structural Representations for Semi-Supervised Histopathology Segmentation
Ha-Hieu Pham, Nguyen Lan Vi Vu, Thanh-Huy Nguyen +4
Accurate gland segmentation in histopathology images is essential for cancer diagnosis and prognosis. However, significant variability in Hematoxylin and Eosin (H&E) staining and t…