From the 1 of 13 linked papers with an AI index.
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CG-GLORE: A Conjugate Gradient-Based Global-Local Regularization Network for Sparse-View CT Reconstruction
Tran Xuan Hieu Le, Doanh C. Bui, Vu Trung Duong Le +5
Sparse-view computed tomography (CT) reduces radiation dose by acquiring fewer projection views, but the resulting inverse problem is highly ill-posed and often produces severe str…
CGRL: Concept-Guided Pruning and Representation Learning for Whole-Slide Image Classification
Thuc Huynh, Tuan Le, Doanh C. Bui
The paper introduces CGRL, a framework that uses class-level concept prototypes to prune and guide representation learning of patches in weakly supervised whole-slide image classif…
Toward Efficient Weakly Supervised Semantic Segmentation Using Only Low-Magnification Histopathological Images
Dung Minh Do, Nhat-Thanh Huynh, Duc Minh Huynh +2
Whole-slide images (WSIs) provide rich tissue-level and cellular-level information, but storing and transmitting high-magnification pathology data is resource-intensive. Moreover,…
Continual Model Merging with Test-Time Adaptation for Whole-Slide Image Analysis
Duc-Thanh Le, Doanh C. Bui, Maï K. Nguyen +1
Model merging offers a practical alternative to conventional continual learning by integrating independently fine-tuned models without retaining previous training data. Recent stat…
MergeSurv: Merging-Based Continual Learning for Survival Analysis on Whole-Slide Images
Vu Minh Tran, Doanh C. Bui, Maï K. Nguyen +1
Survival analysis on Whole Slide Images (WSIs) is important in computational pathology for prognosis estimation and treatment planning. However, existing survival models are typica…
MergeSlide: Continual Model Merging and Task-to-Class Prompt-Aligned Inference for Lifelong Learning on Whole Slide Images
Doanh C. Bui, Ba Hung Ngo, Hoai Luan Pham +3
Lifelong learning on Whole Slide Images (WSIs) aims to train or fine-tune a unified model sequentially on cancer-related tasks, reducing the resources and effort required for data…