Dr. Saltz is a pioneer in developing Digital Pathology tools, methods and algorithms with the ultimate goal of extracting and leveraging digitalized Pathology information to better predict cancer outcome and to steer cancer therapy. He is also an expert in high end computing and has developed a variety of highly cited systems software methods.

His research in Pathology spans twenty years and consists of closely coordinated efforts in image analysis, machine learning, database design and high end computing. He has developed tools and methods through years of funded projects supported by a wide range of institutes and agencies including NCI, NLM, NIBIB, NSF, DARPA, AFOSR, NASA, DOD and DOE. His seminal work in digital imaging laid the foundation for digital pathology as it is today. He was the first to develop the “Virtual Microscope,” and pioneered developments in digital pathology whole slide image navigation, data management and computer aided classification.

Dr. Saltz’s initial efforts included development of the first whole slide image viewer, and devising efficient methods for management, caching and supporting analytics carried out on whole slide datasets. This work became the foundation of the new field of Pathology Imaging Informatics, today investigators he mentored can be found carrying out exciting research in institutions across the country.

Over the years, Dr. Saltz has developed a rich set of Pathology informatics tools, methods and algorithms. Dr. Saltz's team applies generative AI models to digital pathology, developing systems that can create realistic microscopic tissue images. Their diffusion models can generate pathology images from text descriptions and synthesize large-scale tissue samples without requiring time-consuming manual annotations. The team's ZoomLDM system can create gigapixel-sized images that maintain both microscopic detail and overall tissue structure across different magnification levels—similar to how a pathologist would zoom in and out when examining slides. These AI models have shown utility for disease diagnosis, with their learned features performing better than existing methods in detecting breast cancer and genetic mutations. Through their Gen-SIS framework, the team has demonstrated how synthetic images can enhance AI training without additional human labeling, which could help develop diagnostic tools while reducing the burden on medical professionals.

The team has developed a variety of methods for interpretable AI. SI-MIL (Self-Interpretable MIL) integrates handcrafted pathological features into a linear prediction branch for Multiple Instance Learning, enabling interpretable predictions through human-understandable descriptors like tumor cellularity and necrosis rather than opaque attention maps. GECKO (Gigapixel Vision-Concept Contrastive Pretraining) aligns whole slide images with pathology concept priors through contrastive learning, producing concept-aware embeddings that allow pathologists to inspect predictions via concept activation maps. HIPPO (Histopathology Interpretability via Prototypes and Perturbations of WSI) leverages prototypical learning and counterfactual explanations to enable visual interpretation of model decisions through human-understandable image patches and perturbation-based feature importance. These frameworks demonstrate that computational pathology models can achieve strong performance while maintaining clinical interpretability through concept-based reasoning and visual explanations. These frameworks demonstrate that computational pathology models can achieve strong performance while maintaining clinical interpretability through concept-based reasoning.