Zobrazeno 1 - 10
of 341
pro vyhledávání: '"Zaiane, Osmar"'
Mix-up is a key technique for consistency regularization-based semi-supervised learning methods, generating strong-perturbed samples for strong-weak pseudo-supervision. Existing mix-up operations are performed either randomly or with predefined rules
Externí odkaz:
http://arxiv.org/abs/2407.21586
Text style transfer, an important research direction in natural language processing, aims to adapt the text to various preferences but often faces challenges with limited resources. In this work, we introduce a novel method termed Style Extraction an
Externí odkaz:
http://arxiv.org/abs/2407.15556
Autor:
Schoepp, Sheila, Taghian, Mehran, Miwa, Shotaro, Mitsuka, Yoshihiro, Golestan, Shadan, Zaïane, Osmar
Industry is rapidly moving towards fully autonomous and interconnected systems that can detect and adapt to changing conditions, including machine hardware faults. Traditional methods for adding hardware fault tolerance to machines involve duplicatin
Externí odkaz:
http://arxiv.org/abs/2407.15283
The existing barely-supervised medical image segmentation (BSS) methods, adopting a registration-segmentation paradigm, aim to learn from data with very few annotations to mitigate the extreme label scarcity problem. However, this paradigm poses a ch
Externí odkaz:
http://arxiv.org/abs/2407.05248
Autor:
Huang, Chenyang, Ghaddar, Abbas, Kobyzev, Ivan, Rezagholizadeh, Mehdi, Zaiane, Osmar R., Chen, Boxing
Recently, there has been considerable attention on detecting hallucinations and omissions in Machine Translation (MT) systems. The two dominant approaches to tackle this task involve analyzing the MT system's internal states or relying on the output
Externí odkaz:
http://arxiv.org/abs/2406.01919
This paper investigates an extremely challenging problem: barely-supervised volumetric medical image segmentation (BSS). A BSS training dataset consists of two parts: 1) a barely-annotated labeled set, where each labeled image contains only a single-
Externí odkaz:
http://arxiv.org/abs/2405.09777
Autor:
Hou, Qingshan, Cheng, Shuai, Cao, Peng, Yang, Jinzhu, Liu, Xiaoli, Zaiane, Osmar R., Tham, Yih Chung
Representation learning offers a conduit to elucidate distinctive features within the latent space and interpret the deep models. However, the randomness of lesion distribution and the complexity of low-quality factors in medical images pose great ch
Externí odkaz:
http://arxiv.org/abs/2404.04887
Most state-of-the-art methods for medical image segmentation adopt the encoder-decoder architecture. However, this U-shaped framework still has limitations in capturing the non-local multi-scale information with a simple skip connection. To solve the
Externí odkaz:
http://arxiv.org/abs/2312.15182
In this work we search for best practices in pre-processing of Electrocardiogram (ECG) signals in order to train better classifiers for the diagnosis of heart conditions. State of the art machine learning algorithms have achieved remarkable results i
Externí odkaz:
http://arxiv.org/abs/2311.04229
Retinal fundus images have been applied for the diagnosis and screening of eye diseases, such as Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). However, both low-quality fundus images and style inconsistency potentially increase uncertain
Externí odkaz:
http://arxiv.org/abs/2301.06943