Wenjia Wu — accepted AI-conference papers

2026

Factorization-in-Loop:Proximal Fill-in Minimization for Sparse Matrix Reordering

Ziwei Li, Shuzi Niu, Tao Yuan, Huiyuan Li, Wenjia Wu

AAAI 2026technical

Fill-ins are new nonzero elements in the summation of the upper and lower triangular factors generated during LU factorization. For large sparse matrices, they will increase the memory usage and computational time, and be reduced through proper row or column arrangement, namely matrix reordering. Fi

Cited by 0 Source ↗PDF ↗Scholar ↗

2024

PaCaS-WAA: Patch-Based Contrastive Semi-Supervised Learning with Wavelet Guidance and Adaptive Augmentation for Tumour Segmentation

Wanqing Xiong, Zailiang Chen, Qing Liu, Wenjia Wu, Jian Zhang, Hailan Shen

ICASSP 2024accepted

In many image-guided clinical approaches, tumor segmentation is a fundamental and critical step for locating tumor involvement. However, the scarcity of annotated data and the low contrast of medical imaging techniques make it challenging to accurately segment tumors from surrounding tissues using s…

Cited by 0 Source ↗Scholar ↗