PathAsst: Generative Foundation AI Assistant for Pathology
PathAsst is a powerful multimodal AI assistant for pathology, utilizing over 207,000 high-quality image-text pairs and advanced instruction-following techniques to significantly enhance diagnostic and predictive analytics in pathology.
Jan 17, 2024
DPA-P2PNet: Deformable Proposal-Aware P2PNet for Accurate Point-Based Cell Detection
Jan 1, 2024
Task-specific fine-tuning via variational information bottleneck for weakly-supervised pathology whole slide image classification
Jan 1, 2023
Exploring Unsupervised Cell Recognition with Prior Self-activation Maps
Jan 1, 2023
Assessing the Robustness of Deep Learning-Assisted Pathological Image Analysis Under Practical Variables of Imaging System
Jan 1, 2023
Weakly supervised learning for cell recognition in immunohistochemical cytoplasm staining images
Jan 1, 2022
Weakly Supervised Classification using Multi-Level Instance-Aware Optimization on Cervical Cytologic Image
Jan 1, 2022
End-to-end cell recognition by point annotation
Jan 1, 2022
Chrsnet: Chromosome straightening using self-attention guided networks
Jan 1, 2022
Category Separation For Weakly Supervised Multi-Class Cell Counting
Jan 1, 2022