Results 31 to 40 of about 52,973 (279)

Y-Net: A deep Convolutional Neural Network for Polyp Detection [PDF]

CoRR, 2018
Colorectal polyps are important precursors to colon cancer, the third most common cause of cancer mortality for both men and women. It is a disease where early detection is of crucial importance. Colonoscopy is commonly used for early detection of cancer and precancerous pathology.
Ahmed Kedir Mohammed, Sule Yildirim, Ivar Farup, Marius Pedersen, Øistein Hovde   +4 more
openaire   +2 more sources

Bayesian Topological Convolutional Neural Nets

CoRR
Convolutional neural networks (CNNs) have been established as the main workhorse in image data processing; nonetheless, they require large amounts of data to train, often produce overconfident predictions, and frequently lack the ability to quantify the uncertainty of their predictions.
Sarah Harkins Dayton, Hayden Everett, Ioannis D. Schizas, David L. Boothe, Vasileios Maroulas   +4 more
openaire   +2 more sources

A deep LSTM‐CNN based on self‐attention mechanism with input data reduction for short‐term load forecasting

IET Generation, Transmission & Distribution, 2023
Numerous studies on short‐term load forecasting (STLF) have used feature extraction methods to increase the model's accuracy by incorporating multidimensional features containing time, weather and distance information.
Shiyan Yi, Haichun Liu, Tao Chen, Jianwen Zhang, Yibo Fan   +4 more
doaj   +1 more source

xUnit: Learning a Spatial Activation Function for Efficient Image Restoration

, 2018
In recent years, deep neural networks (DNNs) achieved unprecedented performance in many low-level vision tasks. However, state-of-the-art results are typically achieved by very deep networks, which can reach tens of layers with tens of millions of ...
Kligvasser, Idan, Michaeli, Tomer, Shaham, Tamar Rott   +2 more
core   +1 more source

Network Inversion of Convolutional Neural Nets

CoRR
Neural networks have emerged as powerful tools across various applications, yet their decision-making process often remains opaque, leading to them being perceived as "black boxes." This opacity raises concerns about their interpretability and reliability, especially in safety-critical scenarios.
Pirzada Suhail, Amit Sethi
openaire   +2 more sources

End‐to‐end global to local convolutional neural network learning for hand pose recovery in depth data

IET Computer Vision, 2022
Despite recent advances in 3‐D pose estimation of human hands, thanks to the advent of convolutional neural networks (CNNs) and depth cameras, this task is still far from being solved in uncontrolled setups.
Meysam Madadi, Sergio Escalera, Xavier Baró, Jordi Gonzàlez   +3 more
doaj   +1 more source

Efficient Yet Deep Convolutional Neural Networks for Semantic Segmentation

, 2018
Semantic Segmentation using deep convolutional neural network pose more complex challenge for any GPU intensive task. As it has to compute million of parameters, it results to huge memory consumption.
Kamran, Sharif Amit, Sabbir, Ali Shihab
core   +1 more source

BitFlow-Net: Toward Fully Binarized Convolutional Neural Networks [PDF]

IEEE Access, 2019
Binarization can greatly compress and accelerate deep convolutional neural networks (CNNs) for real-time industrial applications. However, existing binarized CNNs (BCNNs) rely on scaling factor (SF) and batch normalization (BatchNorm) that still involve resource-consuming floating-point multiplication operations.
Lijun Wu 0002, Peiqing Jiang, Zhicong Chen, Xu Lin, Yunfeng Lai, Peijie Lin, Shuying Cheng   +6 more
openaire   +2 more sources

Artificial Intelligence in Systemic Sclerosis: Clinical Applications, Challenges, and Future Directions

Arthritis Care &Research, EarlyView.
Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos, José Miguel Sequí‐Sabater, Diego Benavent   +2 more
wiley   +1 more source

Generalizable and efficient cross‐domain person re‐identification model using deep metric learning

IET Computer Vision, 2023
Most of the successful person re‐ID models conduct supervised training and need a large number of training data. These models fail to generalise well on unseen unlabelled testing sets.
Saba Sadat Faghih Imani, Kazim Fouladi‐Ghaleh, Hossein Aghababa   +2 more
doaj   +1 more source