Name
Title | ||
|---|---|---|
Gland Segmentation In Histopathology Images Using Deep Networks And Handcrafted Features |
Abstract | ||
|---|---|---|
Histopathology images contain essential information for medical diagnosis and prognosis of cancerous disease. Segmentation of glands in histopathology images is a primary step for analysis and diagnosis of an unhealthy patient. Due to the widespread application and the great success of deep neural networks in intelligent medical diagnosis and histopathology, we propose a modified version of LinkNet for gland segmentation and recognition of malignant cases. We show that using specific handcrafted features such as invariant local binary pattern drastically improves the system performance. The experimental results demonstrate the competency of the proposed system against the state-of-the-art methods. We achieved the best results in testing on section B images of the Warwick-QU dataset and obtained comparable results on section A images. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8856776 | 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Computer vision,Segmentation,Computer science,Histopathology,Local binary patterns,Feature extraction,Image segmentation,Artificial intelligence,Deep neural networks,Medical diagnosis | Conference | 2019 |
ISSN | Citations | PageRank |
1557-170X | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Safiyeh Rezaei | 1 | 0 | 0.34 |
| Ali Emami | 2 | 8 | 8.05 |
| Hamidreza Zarrabi | 3 | 0 | 1.69 |
| Shima Rafiei | 4 | 3 | 2.78 |
| Kayvan Najarian | 5 | 262 | 59.53 |
| Nader Karimi | 6 | 145 | 32.75 |
| Shadrokh Samavi | 7 | 233 | 38.99 |
| S. M. R. Soroushmehr | 8 | 71 | 21.08 |