| |
Records |
Links |
|
Author |
Ana Garcia Rodriguez; Yael Tudela; Henry Cordova; S. Carballal; I. Ordas; L. Moreira; E. Vaquero; O. Ortiz; L. Rivero; F. Javier Sanchez; Miriam Cuatrecasas; Maria Pellise; Jorge Bernal; Gloria Fernandez Esparrach |
|
| |
Title |
In vivo computer-aided diagnosis of colorectal polyps using white light endoscopy |
Type |
Journal Article |
| |
Year |
2022 |
Publication |
Endoscopy International Open |
Abbreviated Journal |
ENDIO |
|
| |
Volume |
10 |
Issue |
9 |
Pages |
E1201-E1207 |
|
| |
Keywords |
|
|
| |
Abstract |
Background and study aims Artificial intelligence is currently able to accurately predict the histology of colorectal polyps. However, systems developed to date use complex optical technologies and have not been tested in vivo. The objective of this study was to evaluate the efficacy of a new deep learning-based optical diagnosis system, ATENEA, in a real clinical setting using only high-definition white light endoscopy (WLE) and to compare its performance with endoscopists. Methods ATENEA was prospectively tested in real life on consecutive polyps detected in colorectal cancer screening colonoscopies at Hospital Clínic. No images were discarded, and only WLE was used. The in vivo ATENEA's prediction (adenoma vs non-adenoma) was compared with the prediction of four staff endoscopists without specific training in optical diagnosis for the study purposes. Endoscopists were blind to the ATENEA output. Histology was the gold standard. Results Ninety polyps (median size: 5 mm, range: 2-25) from 31 patients were included of which 69 (76.7 %) were adenomas. ATENEA correctly predicted the histology in 63 of 69 (91.3 %, 95 % CI: 82 %-97 %) adenomas and 12 of 21 (57.1 %, 95 % CI: 34 %-78 %) non-adenomas while endoscopists made correct predictions in 52 of 69 (75.4 %, 95 % CI: 60 %-85 %) and 20 of 21 (95.2 %, 95 % CI: 76 %-100 %), respectively. The global accuracy was 83.3 % (95 % CI: 74%-90 %) and 80 % (95 % CI: 70 %-88 %) for ATENEA and endoscopists, respectively. Conclusion ATENEA can accurately be used for in vivo characterization of colorectal polyps, enabling the endoscopist to make direct decisions. ATENEA showed a global accuracy similar to that of endoscopists despite an unsatisfactory performance for non-adenomatous lesions. |
|
| |
Address |
2022 Sep 14 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
PMID |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ISE; 600.157 |
Approved |
no |
|
| |
Call Number  |
Admin @ si @ GTC2022b |
Serial |
3752 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Wenjuan Gong; Zhang Yue; Wei Wang; Cheng Peng; Jordi Gonzalez |
|
| |
Title |
Meta-MMFNet: Meta-Learning Based Multi-Model Fusion Network for Micro-Expression Recognition |
Type |
Journal Article |
| |
Year |
2022 |
Publication |
ACM Transactions on Multimedia Computing, Communications, and Applications |
Abbreviated Journal |
ACMTMC |
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Feature Fusion; Model Fusion; Meta-Learning; Micro-Expression Recognition |
|
| |
Abstract |
Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method. |
|
| |
Address |
May 2022 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ISE; 600.157 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GYW2022 |
Serial |
3692 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Wenjuan Gong; W.Zhang; Jordi Gonzalez; Y.Ren; Z.Li |
|
| |
Title |
Enhanced Asymmetric Bilinear Model for Face Recognition |
Type |
Journal Article |
| |
Year |
2015 |
Publication |
International Journal of Distributed Sensor Networks |
Abbreviated Journal |
IJDSN |
|
| |
Volume |
|
Issue |
|
Pages |
Article ID 218514 |
|
| |
Keywords |
|
|
| |
Abstract |
Bilinear models have been successfully applied to separate two factors, for example, pose variances and different identities in face recognition problems. Asymmetric model is a type of bilinear model which models a system in the most concise way. But seldom there are works exploring the applications of asymmetric bilinear model on face recognition problem with illumination changes. In this work, we propose enhanced asymmetric model for illumination-robust face recognition. Instead of initializing the factor probabilities randomly, we initialize them with nearest neighbor method and optimize them for the test data. Above that, we update the factor model to be identified. We validate the proposed method on a designed data sample and extended Yale B dataset. The experiment results show that the enhanced asymmetric models give promising results and good recognition accuracies. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ISE; 600.063; 600.078 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GZG2015 |
Serial |
2592 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Wenjuan Gong; Xuena Zhang; Jordi Gonzalez; Andrews Sobral; Thierry Bouwmans; Changhe Tu; El-hadi Zahzah |
|
| |
Title |
Human Pose Estimation from Monocular Images: A Comprehensive Survey |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
|
| |
Volume |
16 |
Issue |
12 |
Pages |
1966 |
|
| |
Keywords |
human pose estimation; human bodymodels; generativemethods; discriminativemethods; top-down methods; bottom-up methods |
|
| |
Abstract |
Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling
methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ISE; 600.098; 600.119 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GZG2016 |
Serial |
2933 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Wenjuan Gong; Yue Zhang; Wei Wang; Peng Cheng; Jordi Gonzalez |
|
| |
Title |
Meta-MMFNet: Meta-learning-based Multi-model Fusion Network for Micro-expression Recognition |
Type |
Journal Article |
| |
Year |
2023 |
Publication |
ACM Transactions on Multimedia Computing, Communications, and Applications |
Abbreviated Journal |
TMCCA |
|
| |
Volume |
20 |
Issue |
2 |
Pages |
1–20 |
|
| |
Keywords |
|
|
| |
Abstract |
Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning-based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ISE |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GZW2023 |
Serial |
3862 |
|
| Permanent link to this record |
