1 citations · 2 across the 3 of their papers we have counts for
4 papers
CellDX AI Autopilot: Agent-Guided Training and Deployment of Pathology Classifiers
Alexey Pchelnikov, Aleksei Pchelnikov
Training AI models for computational pathology currently requires access to expensive whole-slide-image datasets, GPU infrastructure, deep expertise in machine learning, and substa…
HISTAI: An Open-Source, Large-Scale Whole Slide Image Dataset for Computational Pathology
Dmitry Nechaev, Alexey Pchelnikov, Ekaterina Ivanova
Recent advancements in Digital Pathology (DP), particularly through artificial intelligence and Foundation Models, have underscored the importance of large-scale, diverse, and rich…
SPIDER: A Comprehensive Multi-Organ Supervised Pathology Dataset and Baseline Models
Dmitry Nechaev, Alexey Pchelnikov, Ekaterina Ivanova
Advancing AI in computational pathology requires large, high-quality, and diverse datasets, yet existing public datasets are often limited in organ diversity, class coverage, or an…
Hibou: A Family of Foundational Vision Transformers for Pathology
Dmitry Nechaev, Alexey Pchelnikov, Ekaterina Ivanova
Pathology, the microscopic examination of diseased tissue, is critical for diagnosing various medical conditions, particularly cancers. Traditional methods are labor-intensive and…