6 citations · 7 across the 3 of their papers we have counts for
6 papers
ConceptMoE: Concept-Guided Multimodal Mixture of Experts for Interpretable Computational Pathology
Xuan Wang, Zhongling Xu, Gopi Kannedhara +13
Healthcare models are transitioning from unimodal prediction toward multimodal reasoning over heterogeneous diagnostic inputs. In computational pathology, for complex tumor subtype…
Clinically-Informed Modeling for Pediatric Brain Tumor Classification from Whole-Slide Histopathology Images
Joakim Nguyen, Jian Yu, Jinrui Fang +7
Accurate diagnosis of pediatric brain tumors, starting with histopathology, presents unique challenges for deep learning, including severe data scarcity, class imbalance, and fine-…
BrainNormalizer: Anatomy-Informed Pseudo-Healthy Brain Reconstruction from Tumor MRI via Edge-Guided ControlNet
Min Gu Kwak, Yeonju Lee, Hairong Wang +2
Brain tumors induce complex structural deformations that obscure the patient' s original neuroanatomy, making it difficult to distinguish tumor-induced changes from inherent anatom…
SmoothSegNet: A Global-Local Framework for Liver Tumor Segmentation with Clinical KnowledgeInformed Label Smoothing
Hairong Wang, Lingchao Mao, Zihan Zhang +1
Liver cancer is a leading cause of mortality worldwide, and accurate Computed Tomography (CT)-based tumor segmentation is essential for diagnosis and treatment. Manual delineation…
Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review
Lingchao Mao, Hairong Wang, Leland S. Hu +4
Cancer remains one of the most challenging diseases to treat in the medical field. Machine learning has enabled in-depth analysis of rich multi-omics profiles and medical imaging f…
A Novel Hybrid Ordinal Learning Model with Health Care Application
Lujia Wang, Hairong Wang, Yi Su +2
Ordinal learning (OL) is a type of machine learning models with broad utility in health care applications such as diagnosis of different grades of a disease (e.g., mild, modest, se…