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pro vyhledávání: '"Bassi, Pedro R. A. S."'
Autor:
Li, Wenxuan, Qu, Chongyu, Chen, Xiaoxi, Bassi, Pedro R. A. S., Shi, Yijia, Lai, Yuxiang, Yu, Qian, Xue, Huimin, Chen, Yixiong, Lin, Xiaorui, Tang, Yutong, Cao, Yining, Han, Haoqi, Zhang, Zheyuan, Liu, Jiawei, Zhang, Tiezheng, Ma, Yujiu, Wang, Jincheng, Zhang, Guang, Yuille, Alan, Zhou, Zongwei
We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-dimensional CT volumes sourced from 112 hospitals across diverse populations, geographies, and facilities. AbdomenAtlas provides 673K high-quality masks of anatomical
Externí odkaz:
http://arxiv.org/abs/2407.16697
Bias and spurious correlations in data can cause shortcut learning, undermining out-of-distribution (OOD) generalization in deep neural networks. Most methods require unbiased data during training (and/or hyper-parameter tuning) to counteract shortcu
Externí odkaz:
http://arxiv.org/abs/2407.09788
Bias or spurious correlations in image backgrounds can impact neural networks, causing shortcut learning (Clever Hans Effect) and hampering generalization to real-world data. ISNet, a recently introduced architecture, proposed the optimization of Lay
Externí odkaz:
http://arxiv.org/abs/2401.08409
Publikováno v:
Nature Communications 15, 291 (2024)
Features in images' backgrounds can spuriously correlate with the images' classes, representing background bias. They can influence the classifier's decisions, causing shortcut learning (Clever Hans effect). The phenomenon generates deep neural netwo
Externí odkaz:
http://arxiv.org/abs/2202.00232
Autor:
Bassi, Pedro R. A. S., Attux, Romis
Objective: To propose novel SSVEP classification methodologies using deep neural networks (DNNs) and improve performances in single-channel and user-independent brain-computer interfaces (BCIs) with small data lengths. Approach: We propose the utiliz
Externí odkaz:
http://arxiv.org/abs/2109.02165
Autor:
Bassi, Pedro R. A. S., Attux, Romis
Publikováno v:
Research on Biomedical Engineering, Springer (2022)
Purpose: we evaluated the generalization capability of deep neural networks (DNNs), trained to classify chest X-rays as Covid-19, normal or pneumonia, using a relatively small and mixed dataset. Methods: we proposed a DNN to perform lung segmentation
Externí odkaz:
http://arxiv.org/abs/2104.06176
Publikováno v:
Biomedical Signal Processing and Control 67 (2021) 102542
Objective: We used deep convolutional neural networks (DCNNs) to classify electroencephalography (EEG) signals in a steady-state visually evoked potentials (SSVEP) based single-channel brain-computer interface (BCI), which does not require calibratio
Externí odkaz:
http://arxiv.org/abs/2010.06503
Autor:
Bassi, Pedro R. A. S., Attux, Romis
Purpose: We present image classifiers based on Dense Convolutional Networks and transfer learning to classify chest X-ray images according to three labels: COVID-19, pneumonia and normal. Methods: We fine-tuned neural networks pretrained on ImageNet
Externí odkaz:
http://arxiv.org/abs/2005.01578
Akademický článek
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Towards Ignoring Backgrounds and Improving Generalization: a Costless DNN Visual Attention Mechanism
This work introduces an attention mechanism for image classifiers and the corresponding deep neural network (DNN) architecture, dubbed ISNet. During training, the ISNet uses segmentation targets to learn how to find the image's region of interest and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::64dfe360eb4b419311900e6bea6140b1
http://arxiv.org/abs/2202.00232
http://arxiv.org/abs/2202.00232