A Classification Method for the Cellular Images Based on Active Learning and Cross-Modal Transfer Learning
In computer-aided diagnosis (CAD) systems, the automatic classification of the different types of the human epithelial type 2 (HEp-2) cells represents one of the critical steps in the diagnosis procedure of autoimmune diseases. Most of the methods prefer to tackle this task using the supervised learning paradigm. However, the necessity of having thousands of manually annotated examples constitutes a serious concern for the state-of-the-art HEp-2 cells classification methods. We present in this work a method that uses active learning in order to minimize the necessity of annotating the majority of the examples in the dataset. For this purpose, we use cross-modal transfer learning coupled with parallel deep residual networks. First, the parallel networks, which take simultaneously different wavelet coefficients as inputs, are trained in a fully supervised way by using a very small and already annotated dataset. Then, the trained networks are utilized on the targeted dataset, which is quite larger compared to the first one, using active learning techniques in order to only select the images that really need to be annotated among all the examples. The obtained results show that active learning, when mixed with an efficient transfer learning technique, can allow one to achieve a quite pleasant discrimination performance with only a few annotated examples in hands. This will help in building CAD systems by simplifying the burdensome task of labeling images while maintaining a similar performance with the state-of-the-art methods.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2021 |
|---|---|
| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:21 |
|---|---|
| Enthalten in: |
Sensors (Basel, Switzerland) - 21(2021), 4 vom: 20. Feb. |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Vununu, Caleb [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Active learning |
|---|
| Anmerkungen: |
Date Completed 15.03.2021 Date Revised 01.02.2022 published: Electronic Citation Status MEDLINE |
|---|
| doi: |
10.3390/s21041469 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM322297737 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM322297737 | ||
| 003 | DE-627 | ||
| 005 | 20231225181737.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.3390/s21041469 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1074.xml |
| 035 | |a (DE-627)NLM322297737 | ||
| 035 | |a (NLM)33672489 | ||
| 035 | |a (PII)1469 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Vununu, Caleb |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A Classification Method for the Cellular Images Based on Active Learning and Cross-Modal Transfer Learning |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 15.03.2021 | ||
| 500 | |a Date Revised 01.02.2022 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a In computer-aided diagnosis (CAD) systems, the automatic classification of the different types of the human epithelial type 2 (HEp-2) cells represents one of the critical steps in the diagnosis procedure of autoimmune diseases. Most of the methods prefer to tackle this task using the supervised learning paradigm. However, the necessity of having thousands of manually annotated examples constitutes a serious concern for the state-of-the-art HEp-2 cells classification methods. We present in this work a method that uses active learning in order to minimize the necessity of annotating the majority of the examples in the dataset. For this purpose, we use cross-modal transfer learning coupled with parallel deep residual networks. First, the parallel networks, which take simultaneously different wavelet coefficients as inputs, are trained in a fully supervised way by using a very small and already annotated dataset. Then, the trained networks are utilized on the targeted dataset, which is quite larger compared to the first one, using active learning techniques in order to only select the images that really need to be annotated among all the examples. The obtained results show that active learning, when mixed with an efficient transfer learning technique, can allow one to achieve a quite pleasant discrimination performance with only a few annotated examples in hands. This will help in building CAD systems by simplifying the burdensome task of labeling images while maintaining a similar performance with the state-of-the-art methods | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a HEp-2 cell images classification | |
| 650 | 4 | |a active learning | |
| 650 | 4 | |a computer-aided diagnosis | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a pattern recognition | |
| 650 | 4 | |a transfer learning | |
| 700 | 1 | |a Lee, Suk-Hwan |e verfasserin |4 aut | |
| 700 | 1 | |a Kwon, Ki-Ryong |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Sensors (Basel, Switzerland) |d 2007 |g 21(2021), 4 vom: 20. Feb. |w (DE-627)NLM187985170 |x 1424-8220 |7 nnns |
| 773 | 1 | 8 | |g volume:21 |g year:2021 |g number:4 |g day:20 |g month:02 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.3390/s21041469 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 21 |j 2021 |e 4 |b 20 |c 02 | ||