Zobrazeno 1 - 10
of 419
pro vyhledávání: '"Wu, Shandong"'
In breast cancer detection and diagnosis, the longitudinal analysis of mammogram images is crucial. Contemporary models excel in detecting temporal imaging feature changes, thus enhancing the learning process over sequential imaging exams. Yet, the r
Externí odkaz:
http://arxiv.org/abs/2411.00837
Prompt learning for pre-trained Vision-Language Models (VLMs) like CLIP has demonstrated potent applicability across diverse downstream tasks. This lightweight approach has quickly gained traction from federated learning (FL) researchers who seek to
Externí odkaz:
http://arxiv.org/abs/2410.10114
Adversarial data can lead to malfunction of deep learning applications. It is essential to develop deep learning models that are robust to adversarial data while accurate on standard, clean data. In this study, we proposed a novel adversarially robus
Externí odkaz:
http://arxiv.org/abs/2402.08768
Personalized Federated Learning (PFL) represents a promising solution for decentralized learning in heterogeneous data environments. Partial model personalization has been proposed to improve the efficiency of PFL by selectively updating local model
Externí odkaz:
http://arxiv.org/abs/2308.09160
Curriculum learning is a learning method that trains models in a meaningful order from easier to harder samples. A key here is to devise automatic and objective difficulty measures of samples. In the medical domain, previous work applied domain knowl
Externí odkaz:
http://arxiv.org/abs/2302.01243
Personalized federated learning has received an upsurge of attention due to the mediocre performance of conventional federated learning (FL) over heterogeneous data. Unlike conventional FL which trains a single global consensus model, personalized FL
Externí odkaz:
http://arxiv.org/abs/2212.01448
Developing successful artificial intelligence systems in practice depends on both robust deep learning models and large, high-quality data. However, acquiring and labeling data can be prohibitively expensive and time-consuming in many real-world appl
Externí odkaz:
http://arxiv.org/abs/2211.08559
Autor:
Liu, Zhudong, Li, Yilu, Shan, Shiping, Zhang, Min, Yang, Hua, Cheng, Wei, Wei, Xiaowu, Wang, Yushuang, Wu, Shandong
Publikováno v:
In Ecotoxicology and Environmental Safety 1 October 2024 284
Deep learning models have gained remarkable performance on a variety of image classification tasks. However, many models suffer from limited performance in clinical or medical settings when data are imbalanced. To address this challenge, we propose a
Externí odkaz:
http://arxiv.org/abs/2111.10620