3 citations · 5 across the 10 of their papers we have counts for
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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…
On the Compressibility of Quantized Large Language Models
Yu Mao, Weilan Wang, Hongchao Du +2
Deploying Large Language Models (LLMs) on edge or mobile devices offers significant benefits, such as enhanced data privacy and real-time processing capabilities. However, it also…