| Jun 13, 2026 |
We are pleased to announce that our paper Federated Learning for Medical Image Analysis: Methods, Challenges, and Future Directions has been accepted for publication in Advanced Engineering Informatics.
Authors: Thuy Thuy Le, Phuong-Nam Tran, Nhat Truong Pham, Balachandran Manavalan, Li Shen, Choong Seon Hong, and Duc Ngoc Minh Dang.
This work presents a comprehensive review of federated learning for medical image analysis from 2020 to 2025, covering key challenges such as data heterogeneity, privacy preservation, communication efficiency, and limited annotations. The paper also discusses emerging directions including semi-supervised learning, self-supervised learning, domain adaptation, personalized federated learning, and foundation models for healthcare applications.
Congratulations to all authors on this achievement!
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| Apr 4, 2026 |
Our paper has been accepted and is now available online in Knowledge-Based Systems:
Phan Tai Duc; Nhut Minh Nguyen; Khang Phuc Nguyen; Phuong-Nam Tran; Nhat Truong Pham; Linh Le; Choong Seon Hong; Duc Ngoc Minh Dang,
From object difficulty to image scoring: A strategy for active learning in object detection, 2026.
This work proposes a novel strategy to improve active learning in object detection by effectively aggregating object-level difficulty into image-level scoring.
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| Feb 10, 2026 |
Our paper from AiTA Lab has been accepted for publication in EAI Endorsed Transactions on Industrial Networks and Intelligent Systems:
Nhut Minh Nguyen, Trung Thanh Nguyen, Thu Thuy Le, Ngoc-Hanh Dang, Phuong Luu Vo, Lam Thanh Hien, Duc Ngoc Minh Dang,
“HyperDyG: Hypergraph-Driven Dynamic Fusion for Semi-Supervised Multimodal Emotion Recognition,”
EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 2025.
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| Oct 3, 2025 |
Our paper from AiTA Lab has been accepted for publication in “Engineering Applications of Artificial Intelligence”:
Nguyen, Nhut Minh and Nguyen, Thanh Trung and Tran, Phuong-Nam and Lim, Chee Peng and Pham, Nhat Truong and Duc Ngoc Minh Dang, Multi-Modal Fusion in Speech Emotion Recognition: A Comprehensive Review of Methods and Technologies, Engineering Applications of Artificial Intelligence, 2026
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