17 citations · 30 across the 12 of their papers we have counts for
7 papers · 1 filter
Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook
Yuan Ma, Junlin Hou, Chao Zhang +4
Learning from noisy labels remains a major challenge in medical image analysis, where annotation demands expert knowledge and substantial inter-observer variability often leads to…
A Versatile Foundation Model for AI-enabled Mammogram Interpretation
Fuxiang Huang, Jiayi Zhu, Yunfang Yu +20
Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer-related mortality in women globally. Mammography is essential for the early detection and diagno…
A Unified Low-level Foundation Model for Enhancing Pathology Image Quality
Ziyi Liu, Zhe Xu, Jiabo Ma +7
Foundation models have revolutionized computational pathology by achieving remarkable success in high-level diagnostic tasks, yet the critical challenge of low-level image enhancem…
Segment Anything in Pathology Images with Natural Language
Zhixuan Chen, Junlin Hou, Liqi Lin +6
Pathology image segmentation is crucial in computational pathology for analyzing histological features relevant to cancer diagnosis and prognosis. However, current methods face maj…
Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks
Junlin Hou, Sicen Liu, Yequan Bie +4
The increasing demand for transparent and reliable models, particularly in high-stakes decision-making areas such as medical image analysis, has led to the emergence of eXplainable…
HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classification
Cheng Jin, Luyang Luo, Huangjing Lin +2
Fine-grained classification of whole slide images (WSIs) is essential in precision oncology, enabling precise cancer diagnosis and personalized treatment strategies. The core of th…
Concept Complement Bottleneck Model for Interpretable Medical Image Diagnosis
Hongmei Wang, Junlin Hou, Hao Chen
Models based on human-understandable concepts have received extensive attention to improve model interpretability for trustworthy artificial intelligence in the field of medical im…