Whole Slide Image
Whole slide images (WSIs) are gigapixel-scale digital representations of tissue samples, crucial for pathology. Research focuses on developing efficient and accurate algorithms, often employing deep learning architectures like transformers and graph neural networks within multiple instance learning (MIL) frameworks, to classify WSIs, predict patient outcomes (e.g., survival, treatment response), and detect biomarkers from the images. These advancements aim to improve diagnostic accuracy, personalize treatment strategies, and accelerate the analysis of large WSI datasets, ultimately impacting both research and clinical practice in pathology.
Papers
Cell Maps Representation For Lung Adenocarcinoma Growth Patterns Classification In Whole Slide Images
Arwa Al-Rubaian, Gozde N. Gunesli, Wajd A. Althakfi, Ayesha Azam, Nasir Rajpoot, Shan E Ahmed Raza
WsiCaption: Multiple Instance Generation of Pathology Reports for Gigapixel Whole-Slide Images
Pingyi Chen, Honglin Li, Chenglu Zhu, Sunyi Zheng, Zhongyi Shui, Lin Yang
Deep learning-based detection of morphological features associated with hypoxia in H&E breast cancer whole slide images
Petru Manescu, Joseph Geradts, Delmiro Fernandez-Reyes
Long-MIL: Scaling Long Contextual Multiple Instance Learning for Histopathology Whole Slide Image Analysis
Honglin Li, Yunlong Zhang, Chenglu Zhu, Jiatong Cai, Sunyi Zheng, Lin Yang