Automated Author ProfileCHU, Chui Shan
University of Hong Kong0000-0002-3660-5568
CHU, Chui Shan
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.8 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The oral tissue sections were stained with hematoxylin and eosin dye. We scanned the stained sections to obtain brightfield and confocal images. The images were annotated to acquire the region of interest (ROI). The ROI images were used to produce patches, and these were center-cropped and downsampled. All the image patches were saved in .png format. To develop and validate deep learning models, the images were segregated at the patient level into 70% training, 10% validation, and 20% testing. The official code that uses this dataset is available on Multiple Instance Learning for Early Recurrence Prediction in Oral Squamous Cell Carcinoma.
Authors
- CHU, Chui Shan ;
- SALAMEH, IBRAHIM MUSTAFA MOHAMMAD ;
- Fatima, Sarwat ;
- Lo, Anthony ;
- Li, Kar Yan ;
- Xie, Shujie ;
- Thomson, Peter ;
- Ho, Joshua W. K. ;
- Sung, Wing-Kin ;
- Oner, Mustafa Umit ;
- Lee, Nikki P. ;
- Zheng, Li Wu
The oral tissue sections were stained with hematoxylin and eosin dye. We scanned the stained sections to obtain brightfield and confocal images. The images were annotated to acquire the region of interest (ROI). The ROI images were used to produce patches, and these were center-cropped and downsampled. All the image patches were saved in .png format. To develop and validate deep learning models, the images were segregated at the patient level into 70% training, 10% validation, and 20% testing. The official code that uses this dataset is available on Multiple Instance Learning for Early Recurrence Prediction in Oral Squamous Cell Carcinoma.
Authors
- CHU, Chui Shan ;
- SALAMEH, IBRAHIM MUSTAFA MOHAMMAD ;
- Fatima, Sarwat ;
- Lo, Anthony ;
- Li, Kar Yan ;
- Xie, Shujie ;
- Thomson, Peter ;
- Ho, Joshua W. K. ;
- Sung, Wing-Kin ;
- Oner, Mustafa Umit ;
- Lee, Nikki P. ;
- Zheng, Li Wu