Update README.md

README.md

@@ -34,17 +34,15 @@ The diagnosis class includes four specific categories: caries, deep caries, peri
 
 ## Annotation Protocol
 
-The DENTEX provides three hierarchically annotated datasets that facilitate various dental detection tasks: (1) quadrant-only for quadrant detection, (2) quadrant-enumeration for tooth detection, and (3) quadrant-enumeration-diagnosis for abnormal tooth detection. Although it may seem redundant to provide a quadrant detection dataset, it is crucial for utilizing the FDI Numbering System. The FDI system is a globally-used system that assigns each quadrant of the mouth a number from 1 through 4. The top right is 1, the top left is 2, the bottom left is 3, and the bottom right is 4. Then each of the eight teeth and each molar are numbered 1 through 8. The 1 starts at the front middle tooth, and the numbers rise the farther back we go. So for example, the back tooth on the lower left side would be 48 according to FDI notation, which means quadrant 4, number 8. Therefore, the quadrant segmentation dataset can significantly simplify the dental enumeration task, even though evaluations will be made only on the fully annotated third data.
+The DENTEX dataset provides three hierarchically annotated datasets that facilitate various dental detection tasks: (1) quadrant-only for quadrant detection, (2) quadrant-enumeration for tooth detection, and (3) quadrant-enumeration-diagnosis for abnormal tooth detection. The quadrant segmentation dataset can significantly simplify the dental enumeration task, despite evaluations being made only on the fully annotated third dataset.
 
 ## Data Split for Evaluation and Training
 
 The DENTEX 2023 dataset comprises three types of data: (a) partially annotated quadrant data, (b) partially annotated quadrant-enumeration data, and (c) fully annotated quadrant-enumeration-diagnosis data. The first two types of data are intended for training and development purposes, while the third type is used for training and evaluations.
 
-To comply with standard machine learning practices, the fully annotated third dataset, consisting of 1005 panoramic X-rays, is partitioned into training, validation, and testing subsets, comprising 705, 50, and 250 images, respectively. Ground truth labels are provided only for the training data, while the validation data is provided without associated ground truth, and the testing data is kept hidden from participants. But, now all the ground truth data is available for researchers.
-
-Note: The datasets are fully identical to the data used for our baseline method, named HierarchicalDet. Therefore, please visit the [MICCAI paper](https://conferences.miccai.org/2023/papers/205-Paper2550.html) and the [GitHub repository](https://github.com/ibrahimethemhamamci/DENTEX) of HierarchicalDet (Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays) for more info.
-
+To comply with standard machine learning practices, the fully annotated third dataset, consisting of 1005 panoramic X-rays, is partitioned into training, validation, and testing subsets, comprising 705, 50, and 250 images, respectively. Ground truth labels are provided only for the training data, while the validation data is provided without associated ground truth. All the ground truth data is now available for researchers.
 
+Note: The datasets are fully identical to the data used for our baseline method, named HierarchicalDet. For more information, please visit the [MICCAI paper](https://conferences.miccai.org/2023/papers/205-Paper2550.html) and the [GitHub repository](https://github.com/ibrahimethemhamamci/DENTEX) of HierarchicalDet (Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays).
 ## Citing Us
 If you use DENTEX, we would appreciate references to the following papers:
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