Zobrazeno 1 - 10
of 388
pro vyhledávání: '"Garibaldi, Jonathan"'
ESP-MedSAM: Efficient Self-Prompting SAM for Universal Domain-Generalized Medical Image Segmentation
Autor:
Xu, Qing, Li, Jiaxuan, He, Xiangjian, Liu, Ziyu, Chen, Zhen, Duan, Wenting, Li, Chenxin, He, Maggie M., Tesema, Fiseha B., Cheah, Wooi P., Wang, Yi, Qu, Rong, Garibaldi, Jonathan M.
The universality of deep neural networks across different modalities and their generalization capabilities to unseen domains play an essential role in medical image segmentation. The recent Segment Anything Model (SAM) has demonstrated its potential
Externí odkaz:
http://arxiv.org/abs/2407.14153
Since their introduction, fuzzy sets and systems have become an important area of research known for its versatility in modelling, knowledge representation and reasoning, and increasingly its potential within the context explainable AI. While the app
Externí odkaz:
http://arxiv.org/abs/2403.12308
Autor:
Salles, Rebecca, Lima, Janio, Coutinho, Rafaelli, Pacitti, Esther, Masseglia, Florent, Akbarinia, Reza, Chen, Chao, Garibaldi, Jonathan, Porto, Fabio, Ogasawara, Eduardo
Time series event detection methods are evaluated mainly by standard classification metrics that focus solely on detection accuracy. However, inaccuracy in detecting an event can often result from its preceding or delayed effects reflected in neighbo
Externí odkaz:
http://arxiv.org/abs/2304.00439
Publikováno v:
Remote Sens. 2022, 14, 3361
In supervised deep learning, learning good representations for remote--sensing images (RSI) relies on manual annotations. However, in the area of remote sensing, it is hard to obtain huge amounts of labeled data. Recently, self--supervised learning s
Externí odkaz:
http://arxiv.org/abs/2107.05948
Publikováno v:
In Information Sciences March 2024 661
This paper presents a novel meta learning framework for feature selection (FS) based on fuzzy similarity. The proposed method aims to recommend the best FS method from four candidate FS methods for any given dataset. This is achieved by firstly const
Externí odkaz:
http://arxiv.org/abs/2005.09856
In this paper, we propose a novel weighted combination feature selection method using bootstrap and fuzzy sets. The proposed method mainly consists of three processes, including fuzzy sets generation using bootstrap, weighted combination of fuzzy set
Externí odkaz:
http://arxiv.org/abs/2005.05003
In this paper, based on a fuzzy entropy feature selection framework, different methods have been implemented and compared to improve the key components of the framework. Those methods include the combinations of three ideal vector calculations, three
Externí odkaz:
http://arxiv.org/abs/2005.04888
Autor:
Jafari, Mina, Li, Ruizhe, Xing, Yue, Auer, Dorothee, Francis, Susan, Garibaldi, Jonathan, Chen, Xin
Publikováno v:
The 10th International Conference on Image and Graphics (ICIG 2019)
In this paper, we present a generic deep convolutional neural network (DCNN) for multi-class image segmentation. It is based on a well-established supervised end-to-end DCNN model, known as U-net. U-net is firstly modified by adding widely used batch
Externí odkaz:
http://arxiv.org/abs/2004.13470
Publikováno v:
2020 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2020)
Residual network (ResNet) and densely connected network (DenseNet) have significantly improved the training efficiency and performance of deep convolutional neural networks (DCNNs) mainly for object classification tasks. In this paper, we propose an
Externí odkaz:
http://arxiv.org/abs/2004.13453