In this paper, we describe the participation of the Mami team at ImageCLEF 2017 for the Image Caption task. We participated to the concept detection subtask which aims at assigning a set of concept labels to a medical image. We used transfer learning method with VGG19 model for feature extraction to solve this task, and apply those features as input of a new neural network
Addressing issues related to multi-label classification is relevant in many fields of applications. ...
Deep neural networks have shown increasing performance in image classification recent years. However...
This paper presents an overview of the ImageCLEF 2017 caption tasks on the analysis of images from t...
International audienceIn this paper, we describe the participation of the Mami team at ImageCLEF 201...
This paper describes the ImageCLEF 2019 Concept Detection Task. This is the 3rd edition of the medic...
This paper describes the participation of the U.S. National Library of Medicine (NLM) in the ImageCL...
This work presents the NLIP-Essex-ITESM team's participation in the concept detection sub-task of th...
This work presents the proposed solutions of our team for the ImageCLEFmedical Caption 2022 task [1...
xviii, 122 p. : ill. (some col.) ; 30 cm.PolyU Library Call No.: [THS] LG51 .H577P EIE 2014 ChenThis...
The University of Essex participated in the fourth edition of the ImageCLEFcaption task which aims t...
This paper describes the ImageCLEFmed 2020 Concept Detection Task. After first being proposed at Ima...
ImageCLEF is the image retrieval task of the Conference and Labs of the Evaluation Forum (CL...
Recent studies on multi-label image classification have focused on designing more complex architect...
Conference of 16th Conference and Labs of the Evaluation Forum, CLEF 2015 ; Conference Date: 8 Septe...
Multi-label image classification is a foundational topic in various domains. Multimodal learning app...
Addressing issues related to multi-label classification is relevant in many fields of applications. ...
Deep neural networks have shown increasing performance in image classification recent years. However...
This paper presents an overview of the ImageCLEF 2017 caption tasks on the analysis of images from t...
International audienceIn this paper, we describe the participation of the Mami team at ImageCLEF 201...
This paper describes the ImageCLEF 2019 Concept Detection Task. This is the 3rd edition of the medic...
This paper describes the participation of the U.S. National Library of Medicine (NLM) in the ImageCL...
This work presents the NLIP-Essex-ITESM team's participation in the concept detection sub-task of th...
This work presents the proposed solutions of our team for the ImageCLEFmedical Caption 2022 task [1...
xviii, 122 p. : ill. (some col.) ; 30 cm.PolyU Library Call No.: [THS] LG51 .H577P EIE 2014 ChenThis...
The University of Essex participated in the fourth edition of the ImageCLEFcaption task which aims t...
This paper describes the ImageCLEFmed 2020 Concept Detection Task. After first being proposed at Ima...
ImageCLEF is the image retrieval task of the Conference and Labs of the Evaluation Forum (CL...
Recent studies on multi-label image classification have focused on designing more complex architect...
Conference of 16th Conference and Labs of the Evaluation Forum, CLEF 2015 ; Conference Date: 8 Septe...
Multi-label image classification is a foundational topic in various domains. Multimodal learning app...
Addressing issues related to multi-label classification is relevant in many fields of applications. ...
Deep neural networks have shown increasing performance in image classification recent years. However...
This paper presents an overview of the ImageCLEF 2017 caption tasks on the analysis of images from t...