3 citations · 4 across the 6 of their papers we have counts for
6 papers
Easz: An Agile Transformer-based Image Compression Framework for Resource-constrained IoTs
Yu Mao, Jingzong Li, Jun Wang +4
Neural image compression, necessary in various machine-to-machine communication scenarios, suffers from its heavy encode-decode structures and inflexibility in switching between di…
WISE: A Framework for Gigapixel Whole-Slide-Image Lossless Compression
Yu Mao, Jun Wang, Nan Guan +1
Whole-Slide Images (WSIs) have revolutionized medical analysis by presenting high-resolution images of the whole tissue slide. Despite avoiding the physical storage of the slides,…
SHAP-CAT: A interpretable multi-modal framework enhancing WSI classification via virtual staining and shapley-value-based multimodal fusion
Jun Wang, Yu Mao, Nan Guan +1
The multimodal model has demonstrated promise in histopathology. However, most multimodal models are based on H\&E and genomics, adopting increasingly complex yet black-box designs…
Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions
Jun Wang, Yu Mao, Nan Guan +1
Whole slide images (WSIs) are gigapixel-scale digital images of H\&E-stained tissue samples widely used in pathology. The substantial size and complexity of WSIs pose unique analyt…
IHC Matters: Incorporating IHC analysis to H&E Whole Slide Image Analysis for Improved Cancer Grading via Two-stage Multimodal Bilinear Pooling Fusion
Jun Wang, Yu Mao, Yufei Cui +2
Immunohistochemistry (IHC) plays a crucial role in pathology as it detects the over-expression of protein in tissue samples. However, there are still fewer machine learning model s…
BAHOP: Similarity-based Basin Hopping for A fast hyper-parameter search in WSI classification
Jun Wang, Yu Mao, Yufei Cui +2
Pre-processing whole slide images (WSIs) can impact classification performance. Our study shows that using fixed hyper-parameters for pre-processing out-of-domain WSIs can signific…