Automated Author ProfileStathaki, Tania
Imperial College London
Stathaki, Tania
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: 1.6 (sum of 4 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
GAstric GLands (GAGL) dataset. - v.1 GAGL 'minimal dataset' (a part of GAGL dataset) consists of 9 downsampled WSI including 3 normal, 3 gastric atrophic and 3 intestinal metaplasia cases. The minimal dataset could be used for the validation of the study findings of the following study: A digital pathology workflow for the segmentation and classification of gastric glands: Study of gastric atrophy and intestinal metaplasia cases. GAGL dataset consists of 85 WSI, collected from 20 patients. Gastric tissues were collected at University College London Hospital NHS trust, with ethical approval (research ethics committee (REC) reference: 15/YH/0311, & 19/LO/0089), with informed consent taken for prospective tissue collection. Samples were collected prospectively from patients undergoing gastrectomy for cancer, or sleeve gastrectomy for weight loss, with archival tissue used from endoscopic surveillance biopsies. Tissue underwent routine Hematoxylin & Eosin (H&E) staining. More specifically, the dataset includes 14 normal, 26 GA and 45 IM images. Please cite as: To be confirmed.
Authors
- Barmpoutis, Panagiotis ;
- Waddingham, William ;
- Yuan, Jing ;
- Ross, Christopher ;
- Kayhanian Hamzeh ;
- Stathaki, Tania ;
- Alexander, Daniel C. ;
- Jansen, Marnix
GAstric GLands (GAGL) dataset. - v.1 GAGL 'minimal dataset' (a part of GAGL dataset) consists of 9 downsampled WSI including 3 normal, 3 gastric atrophic and 3 intestinal metaplasia cases. The minimal dataset could be used for the validation of the study findings of the following study: A digital pathology workflow for the segmentation and classification of gastric glands: Study of gastric atrophy and intestinal metaplasia cases. GAGL dataset consists of 85 WSI, collected from 20 patients. Gastric tissues were collected at University College London Hospital NHS trust, with ethical approval (research ethics committee (REC) reference: 15/YH/0311, & 19/LO/0089), with informed consent taken for prospective tissue collection. Samples were collected prospectively from patients undergoing gastrectomy for cancer, or sleeve gastrectomy for weight loss, with archival tissue used from endoscopic surveillance biopsies. Tissue underwent routine Hematoxylin & Eosin (H&E) staining. More specifically, the dataset includes 14 normal, 26 GA and 45 IM images. Please cite as: To be confirmed.
Authors
- Barmpoutis, Panagiotis ;
- Waddingham, William ;
- Yuan, Jing ;
- Ross, Christopher ;
- Kayhanian Hamzeh ;
- Stathaki, Tania ;
- Alexander, Daniel C. ;
- Jansen, Marnix
The IMGL (Intestinal Metaplasia gastric GLands) dataset includes 500 normal and 500 gastric glands with intestinal metaplasia. Gastric tissues were collected at University College London Hospital NHS trust, with ethical approval with informed consent taken for prospective tissue collection. The tissues underwent routine H&E staining. Please cite as: Barmpoutis P, Yuan J, Waddingham W, Ross C, Kayhanian H, Stathaki T, Alexander DC, Jansen M (2022). Multi-scale Deformable Transformer for the Classification of Gastric Glands: The IMGL Dataset. Cancer Prevention through early detecTion Workshop, MICCAI 2022, September 22, 2022.
Authors
- Barmpoutis, Panagiotis ;
- Yuan, Jing ;
- Waddingham, William ;
- Ross, Christopher ;
- Kayhanian Hamzeh ;
- Stathaki, Tania ;
- Alexander, Daniel C. ;
- Jansen, Marnix
The IMGL (Intestinal Metaplasia gastric GLands) dataset includes 500 normal and 500 gastric glands with intestinal metaplasia. Gastric tissues were collected at University College London Hospital NHS trust, with ethical approval with informed consent taken for prospective tissue collection. The tissues underwent routine H&E staining. Please cite as: Barmpoutis P, Yuan J, Waddingham W, Ross C, Kayhanian H, Stathaki T, Alexander DC, Jansen M (2022). Multi-scale Deformable Transformer for the Classification of Gastric Glands: The IMGL Dataset. Cancer Prevention through early detecTion Workshop, MICCAI 2022, September 22, 2022.
Authors
- Barmpoutis, Panagiotis ;
- Yuan, Jing ;
- Waddingham, William ;
- Ross, Christopher ;
- Kayhanian Hamzeh ;
- Stathaki, Tania ;
- Alexander, Daniel C. ;
- Jansen, Marnix