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In this work, we propose and develop deconvolution architecture for efficient FPGA implementation. FPGA-based accelerators are proposed for both deconvolution ...

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Optimizing. CNN-based Segmentation with Deeply Customized Convolutional and Deconvolutional Architectures on FPGA. ACM Trans. Reconfig. Technol. Syst. 1, 1 ...

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Optimizing CNN-based Segmentation with Deeply Customized Convolutional and Deconvolutional Architectures on FPGA · Shuanglong Liu, Hongxiang Fan, +3 authors. W.

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Optimizing CNN-based segmentation with deeply customized convolutional and deconvolutional architectures on FPGA ... optimization techniques. A non-linear ...

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Bibliographic details on Optimizing CNN-based Segmentation with Deeply Customized Convolutional and Deconvolutional Architectures on FPGA.

accessible and customizable deep-learning image segmentation

... deep neural networks, many pieces of the process become daunting; optimizing the many user-defined “hyper-parameters” of the algorithm ...

‪Shuanglong Liu‬ - ‪Google 学术搜索‬ - Google Scholar

Optimizing CNN-based segmentation with deeply customized convolutional and deconvolutional architectures on FPGA. S Liu, H Fan, X Niu, H Ng, Y Chu, W Luk. ACM ...

A Novel Approach to Optimizing Convolutional Neural Networks for ...

When it comes to image segmentation, the fundamental idea behind deep learning is to train a convolutional neural network (CNN) to learn a ...

Image Segmentation: Architectures, Losses, Datasets, and ...

al 2017 “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation” | Source ... """ # configuration name NAME = "customized ...

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Convolutional Neural Networks (CNNs): A 2025 Deep Dive - viso.ai

Recent innovations in CNN design focus on optimizing network ... U-Net, a CNN architecture for biomedical image segmentation, is a prime example.

‪Fan Hongxiang‬ - ‪Google Scholar‬

Optimizing CNN-based segmentation with deeply customized convolutional and deconvolutional architectures on FPGA. S Liu, H Fan, X Niu, H Ng, Y Chu, W Luk. ACM ...

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The pre-processed picture is next subjected to entropy-based segmentation algorithms, which separate the image's significant areas in order to distinguish ...

Deep learning for medical image segmentation: State-of-the-art ...

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An Efficient and Optimal Deep Learning Architecture using Custom ...

In this paper, It have proposed a kidney tumor semantic segmentation model based on CU-Net and Mask R-CNN to extract kidney tumor information from abdominal MR ...

A Comprehensive Survey of Convolutions in Deep Learning - arXiv

Liu et al., ”Deep learning based brain tumor segmentation: a survey,” Jul. ... Ikehara, ”GAN-Based Image Deblurring Using DCT Loss With Customized Datasets,” Jan.

Convolutional Neural Networks (CNN) and Deep Learning - Intel

Developing and deploying a CNN model is a complex process with three stages: training, optimizing, and inference. Computer vision combines hardware and software ...

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Customized designs can be optimized, FPGA. Timing latency, Implemented FPGA ... Deep learning-based image segmentation on multimodal medical imaging. IEEE ...