Search
Abstract
The acquisition of high-quality data and annotations is essential for the training of efficient machine learning algorithms, while being an expensive and time-consuming process. Although the process of data processing and training and testing of machine learning models is well studied and considered in the literature, the actual procedures of obtaining data and their annotations in collaboration with physicians are in most cases based on the personal intuition and suppositions of the researchers.
Citations
-
1
CrossRef
-
0
Web of Science
-
1
Scopus
Authors (2)
Cite as
Full text
full text is not available in portal
Keywords
Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language:
- English
- Publication year:
- 2021
- Bibliographic description:
- Cychnerski J., Dziubich T.: Process of Medical Dataset Construction for Machine Learning-Multifield Study and Guidelines// / : , 2021,
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-030-85082-1_20
- Sources of funding:
- Verified by:
- Gdańsk University of Technology
seen 162 times
Recommended for you
Medical Image Dataset Annotation Service (MIDAS)
- B. Klaudel,
- A. Obuchowski,
- B. Rydziński
- + 4 authors
2020
Active Annotation in Evaluating the Credibility of Web-Based Medical Information: Guidelines for Creating Training Data Sets for Machine Learning
- A. Nabożny,
- B. Balcerzak,
- A. Wierzbicki
- + 2 authors
2021