Automated pancreatic mass segmentation in computer tomography images using a voting ensemble method based on encoder-decoder architecturesPancreatic massEnsemble ModelEncoder-Decoder architecturePancreatic cancer poses significant challenges in early diagnosis, with a high mortality rate of 98%, and is ...
A comprehensive analysis was performed to improve the accuracy of the method in SLS. The semantic segmentation method consists of an encoder-decoder architecture, data augmentation, learning schemes, and post-processing methods. To enhance the SLS, we modified the decoder with the bidirectional ...
Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily! python computer-vision deep-learning tensorflow dataset segmentation densenet upsampling semantic-segmentation epoch iou encoder-decoder refinenet semantic-segmentation-models Updated Apr 22, 2021 Pyth...
[Communications in Computer and Information Science] Computational Intelligence and Information Technology Volume 250 || Reed-Solomon Decoder Architecture Using Bit-Parallel Systolic Multiplier 来自 onAcademic 喜欢 0 阅读量: 19 作者:VV Das,N Thankachan ...
The suggested method employs computer vision and deep learning to identify gas meter characters under harsh imaging conditions. It helps to advance the use of deep learning in manufacturing. A unique character recognition model is proposed based on a multi-attention and encoder–decoder architecture. ...
Semantic segmentation with the goal to assign semantic labels to every pixel in an image [1,2,3,4,5] is one of the fundamental topics in computer vision. Deep convolutional neural networks [6,7,8,9,10] based on the Fully Convolutional Neural Network [8,11] show striking improvement over...
We propose a hardware architecture for 50G-PON LDPC decoder achieving high throughput and high error correcting capability while maintaining low level of resource utilization and implementation complexity. Our approach employs phased decoding as a key algorithm which effectively balances the competing goals...
In summary, the CNN approach using CNN of U-Net architecture for automated segmentation of abdominal adipose tissue substantially facilitates data processing and offers the opportunity to automatically discriminate abdominal SAT and VAT compartments. Within the research field of neurodegenerative disorders ...
The main advantage of the new upsampling layer lies in that with a relatively lower-resolution feature map such as 1/16 or 1/32 of the input size, we can achieve even better segmentation accuracy, significantly reducing computation complexity. This is made possible by 1) the new upsampling ...
He was born in 1969 and received his Ph.D. degrees in 1985 from Harbin Institute of Technology. His major research interests include computer architecture, distributed system and accelerator design. Yoshimasa Tsuruoka is an associate professor at Department of Information and Communication Engineering, ...