Name
Title | ||
|---|---|---|
Vectorized persistent homology representations for characterizing glandular architecture in histology images |
Abstract | ||
|---|---|---|
Characterizing glandular architecture in histology images of adenocarcinomas is a fundamental problem in digital pathology, with important implications for computer-assisted diagnosis and grading. In this paper, we present a new set of features for encoding the glandular epithelium architecture based on two recently developed vectorized persistent homology representations called persistence images and persistence landscapes and demonstrate their application to colorectal cancer diagnosis. On the MICCAI2015 Gland Segmentation Challenge Contest dataset with 165 images (85 training, 80 test images), we obtained a benign vs malignant classification accuracy of 85% and 83% using persistence image and persistence landscape based features, respectively. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ISBI.2018.8363562 | 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) |
Keywords | Field | DocType |
Histopathology,Cancer Grading,Persistent homology,Persistence images,Persistence landscapes,Machine learning,Computer aided diagnosis | Computer vision,Architecture,Pattern recognition,Computer science,Segmentation,Computer-aided diagnosis,Digital pathology,Persistent homology,Glandular epithelium,Artificial intelligence,Histology | Conference |
ISSN | ISBN | Citations |
1945-7928 | 978-1-5386-3637-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| D R Chittajallu | 1 | 26 | 3.75 |
| Neal Siekierski | 2 | 1 | 1.64 |
| Sanghoon Lee | 3 | 740 | 97.47 |
| Samuel Gerber | 4 | 205 | 12.82 |
| Jonathan D. Beezley | 5 | 101 | 14.55 |
| David Manthey | 6 | 3 | 1.10 |
| David A. Gutman | 7 | 210 | 14.21 |
| Lee A D Cooper | 8 | 129 | 12.14 |