Name
Title | ||
|---|---|---|
An On-Going Framework for Easily Experimenting with Deep Learning Models for Bioimaging Analysis. |
Abstract | ||
|---|---|---|
Due to the broad use of deep learning methods in Bioimaging, it seems convenient to create a framework that facilitates the task of analysing different models and selecting the best one to solve each particular problem. In this work-in-progress, we are developing a Python framework to deal with such a task in the case of bioimage classification. Namely, the purpose of the framework is to automate and facilitate the process of choosing the best combination of feature extractors (obtained from transfer learning and other techniques), and classification models. The features and models to test are fixed by a simple configuration file to facilitate the use of the framework by non-expert users. The best model is automatically selected through a statistical study, and then it can be employed to predict the category of new images. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-99608-0_39 | DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
Deep learning,Machine learning,Parallelization,Bioimaging,Image processing | Computer science,Transfer of learning,Image processing,Artificial intelligence,Deep learning,Machine learning,Python (programming language),Distributed computing | Conference |
Volume | ISSN | Citations |
801 | 2194-5357 | 0 |
PageRank | References | Authors |
0.34 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manuel García | 1 | 0 | 0.34 |
| César Domínguez | 2 | 95 | 18.93 |
| Jónathan Heras | 3 | 94 | 23.31 |
| Eloy J. Mata | 4 | 11 | 6.38 |
| Vico Pascual | 5 | 58 | 13.19 |