Pathology foundation models (PFMs) have become a common backbone for analyzing whole-slide images, typically through multiple instance learning (MIL). This approach lets a single model handle a wide range of histopathology tasks, from tumor detection to grading. But the abstract for a new arXiv paper notes that these models remain sensitive to cross-hospital distribution shifts — meaning a model trained on slides from one institution may not perform as well on slides from another.
The paper introduces AdaptMIL, a domain adaptation method designed specifically for MIL in digital pathology. While the abstract does not give technical details, the name and framing suggest the method adapts the model to new hospital domains without losing its general utility. The goal is to make PFMs more reliable when deployed across different clinical settings.
Because only the abstract is available, the article does not describe how AdaptMIL works or report experimental results. The significance is in the problem it addresses: distribution shift is a known barrier to clinical adoption of pathology AI, and AdaptMIL is one proposed step toward overcoming it.