Update README.md

README.md

@@ -15,7 +15,8 @@ In digital pathology, whole-slide images (WSIs) are often difficult to handle du
 However, patch-level SSL may overlook complex domain-specific features that are essential for biomarker prediction, such as mutation status and molecular characteristics, as SSL methods rely only on basic augmentations selected for natural image domains on small patch-level area.
 Moreover, SSL methods remain less data efficient than fully supervised approaches, requiring extensive computational resources and datasets to achieve competitive performance.
 To address these limitations, we present EXAONE Path 2.0, a pathology foundation model that learns patch-level representations under direct slide-level supervision.
-Using only 35k WSIs for training, EXAONE Path 2.0 achieves state-of-the-art average performance across 10 biomarker prediction tasks, demonstrating remarkable data efficiency. 
+Using only 35k WSIs for training, EXAONE Path 2.0 achieves state-of-the-art average performance across 10 biomarker prediction tasks, demonstrating remarkable data efficiency.
+For further details, please refer to EXAONE_Path_2_0_technical_report.pdf.
 
 ## Quickstart
 Load EXAONE Path 2.0 and extract features.