Abstract
| Original language | English |
|---|---|
| Title of host publication | International Conference on Computer Analysis of Images and Patterns : CAIP 2019: Computer Analysis of Images and Patterns |
| Editors | Mario Vento, Gennaro Percannella |
| Publisher | Springer |
| Publication date | 22. Aug 2019 |
| Pages | 492-504 |
| ISBN (Print) | 978-3-030-29887-6 |
| ISBN (Electronic) | 978-3-030-29888-3 |
| DOIs | |
| Publication status | Published - 22. Aug 2019 |
| Event | The 18th International Conference on Computer Analysis of Images and Patterns - Duration: 2. Sep 2019 → 6. Sep 2019 https://caip2019.unisa.it/ |
Conference
| Conference | The 18th International Conference on Computer Analysis of Images and Patterns |
|---|---|
| Period | 02/09/2019 → 06/09/2019 |
| Internet address |
| Series | Lecture Notes in Computer Science |
|---|---|
| Volume | 11678 |
| ISSN | 0302-9743 |
Fingerprint
Keywords
- Image recognition
- GoogLeNet
- VGG-16
- ResNet-50
- Transfer learning
- Polyp detection
- HEp-2 image classification
Cite this
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Comparison of Deep Learning-Based Recognition Techniques for Medical and Biomedical Images. / Majtner, Tomas; S. Nadimi, Esmaeil.
International Conference on Computer Analysis of Images and Patterns: CAIP 2019: Computer Analysis of Images and Patterns. ed. / Mario Vento; Gennaro Percannella. Springer, 2019. p. 492-504 (Lecture Notes in Computer Science, Vol. 11678).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
TY - GEN
T1 - Comparison of Deep Learning-Based Recognition Techniques for Medical and Biomedical Images
AU - Majtner, Tomas
AU - S. Nadimi, Esmaeil
PY - 2019/8/22
Y1 - 2019/8/22
N2 - The recognition and classification of medical and biomedical images typically suffer from the problem of a low number of annotated samples. This comes along with the problem of efficient training of the current deep learning frameworks. Therefore, many researchers opt for various techniques which could substitute the traditional training of convolutional neural networks (CNN) from scratch. In this article, we are comparing multiple of these methods, including transfer learning and using the CNNs as feature extractors. The paper contains results on two datasets with different modalities and three different CNN architectures. We demonstrate the high effectiveness of transfer learning and suggest that, in some cases, it is worth retraining more layers at the end of the network for achieving higher accuracy.
AB - The recognition and classification of medical and biomedical images typically suffer from the problem of a low number of annotated samples. This comes along with the problem of efficient training of the current deep learning frameworks. Therefore, many researchers opt for various techniques which could substitute the traditional training of convolutional neural networks (CNN) from scratch. In this article, we are comparing multiple of these methods, including transfer learning and using the CNNs as feature extractors. The paper contains results on two datasets with different modalities and three different CNN architectures. We demonstrate the high effectiveness of transfer learning and suggest that, in some cases, it is worth retraining more layers at the end of the network for achieving higher accuracy.
KW - Image recognition
KW - GoogLeNet
KW - VGG-16
KW - ResNet-50
KW - Transfer learning
KW - Polyp detection
KW - HEp-2 image classification
U2 - 10.1007/978-3-030-29888-3_40
DO - 10.1007/978-3-030-29888-3_40
M3 - Article in proceedings
SN - 978-3-030-29887-6
SP - 492
EP - 504
BT - International Conference on Computer Analysis of Images and Patterns
A2 - Vento, Mario
A2 - Percannella, Gennaro
PB - Springer
ER -