Update README.md

README.md

@@ -60,18 +60,17 @@ The original `PLISM-wsi` subset contains a total of 310,947 images.
 Registration was performed across all scanners and staining conditions using OpenCV's AKAZE (Alcantarilla et al., 2013)  key-point matching algorithm.
 There were 3,417 aligned image groups, with a total of 310,947 (3,417 groups × 91 WSIs) image patches of shape 512x512 at a resolution ranging from 0.22 to 0.26 µm/pixel (40x magnification).
 
-<div style="background-color: #e6f7ff; border-left: 5px solid #ffec99; padding: 10px;">
-    <strong>Note:</strong> To follow the spirit of this unique and outstanding contribution, we generated an extended version of the original tiles dataset provided by (Ochi et al. 2024) so as to ease its adoption accross the digital pathology community and serve as a reference dataset for benchmarking the robustess of foundation models to staining and scanner variations.
-  In particular, our work differs from the original dataset in the following aspects:
-
-  • The original, non-registered WSIs were registered using Elastix (Klein et al., 2010; Shamonin et al., 2014). The reference slide was stained with GMH condition and digitized using Hamamatsu Nanozoomer S60 scanner.
-  
-  • Tiles of 224x224 pixels were extracted at mpp 0.5 µm/pixel (20x magnification) using an in-house bidirectionnal U-Net (Ronneberger et al., 2015).
-  
-  • All tiles from the original WSI were extracted, resulting in 16,278 tiles for each of the 91 WSIs.
-
-**In total, our dataset encompasses 1,481,298 histology tiles for a total size of 150 Gb.**
-</div>
+> [!NOTE]
+> To follow the spirit of this unique and outstanding contribution, we generated an extended version of the original tiles dataset provided by (Ochi et al. 2024) so as to ease its adoption accross the digital pathology community and serve as a reference dataset for benchmarking the robustess of foundation models to staining and scanner variations.
+> In particular, our work differs from the original dataset in the following aspects:
+>
+>  • The original, non-registered WSIs were registered using Elastix (Klein et al., 2010; Shamonin et al., 2014). The reference slide was stained with GMH condition and digitized using Hamamatsu Nanozoomer S60 scanner.
+>  
+>  • Tiles of 224x224 pixels were extracted at mpp 0.5 µm/pixel (20x magnification) using an in-house bidirectionnal U-Net (Ronneberger et al., 2015).
+>  
+>  • All tiles from the original WSI were extracted, resulting in 16,278 tiles for each of the 91 WSIs.
+>
+> **In total, our dataset encompasses 1,481,298 histology tiles for a total size of 150 Gb.**
 
 For each tile, we provide the original slide id (`slide_id`), tile id (`tile_id`), stainer and scanner.