286 citations · 309 across the 7 of their papers we have counts for
8 papers · 1 filter
PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology
Jiabo Ma, Yingxue Xu, Fengtao Zhou +23
The emergence of pathology foundation models has revolutionized computational histopathology, enabling highly accurate, generalized whole-slide image analysis for improved cancer d…
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…
SurgPETL: Parameter-Efficient Image-to-Surgical-Video Transfer Learning for Surgical Phase Recognition
Shu Yang, Zhiyuan Cai, Luyang Luo +3
Capitalizing on image-level pre-trained models for various downstream tasks has recently emerged with promising performance. However, the paradigm of "image pre-training followed b…
OXnet: Omni-supervised Thoracic Disease Detection from Chest X-rays
Luyang Luo, Hao Chen, Yanning Zhou +2
Chest X-ray (CXR) is the most typical diagnostic X-ray examination for screening various thoracic diseases. Automatically localizing lesions from CXR is promising for alleviating r…
Deep Mining External Imperfect Data for Chest X-ray Disease Screening
Luyang Luo, Lequan Yu, Hao Chen +4
Deep learning approaches have demonstrated remarkable progress in automatic Chest X-ray analysis. The data-driven feature of deep models requires training data to cover a large dis…