Results 31 to 40 of about 5,768,655 (280)
U-Net_dc: a novel U-Net-based model for endometrial cancer cell image segmentation
, 2023 Mutated cells may constitute a source of cancer. As an effective approach to quantifying the extent of cancer, cell image segmentation is of particular importance for understanding the mechanism of the disease, observing the degree of cancer cell lesions,
Zhanlin Ji (14016624) +6 more
core +1 more source
Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling [PDF]
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020 Convolutional neural networks (CNNs) with dilated filters such as the Wavenet or the Temporal Convolutional Network (TCN) have shown good results in a variety of sequence modelling tasks. While their receptive field grows exponentially with the number of layers, computing the convolutions over very long sequences of features in each layer is time and ...
Daniel Stoller +3 more
openaire +2 more sources
U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration?
, 2022 Due to their extreme long-range modeling capability, vision transformer-based networks have become increasingly popular in deformable image registration. We believe, however, that the receptive field of a 5-layer convolutional U-Net is sufficient to capture accurate deformations without needing long-range dependencies.
Xi Jia +5 more
openaire +4 more sources
, 2022 U-Net architecture used for the experiments in Table 1. D specifies the base level of channels; we considered experiments with D = 2, 4, 8, 16.
Brett W. Larsen (12902597) +1 more
core +1 more source
Segmentation and recognition of breast ultrasound images based on an expanded U-Net.
PLoS ONE, 2021 This paper establishes a fully automatic real-time image segmentation and recognition system for breast ultrasound intervention robots. It adopts the basic architecture of a U-shaped convolutional network (U-Net), analyses the actual application ...
Yanjun Guo +3 more
doaj +1 more source
Journal of Medical Internet Research, 2022 BackgroundDetection and quantification of intra-abdominal free fluid (ie, ascites) on computed tomography (CT) images are essential processes for finding emergent or urgent conditions in patients.
Hoon Ko +7 more
doaj +1 more source
, 2022 We trained four U-Net architectures of increasing size by each time doubling the number of channels in each layer. For each model, we report the best performance across 10 trained models on 10,240 test samples.
Brett W. Larsen (12902597) +1 more
core +1 more source
U-Net++DSM: Improved U-Net++ for Brain Tumor Segmentation With Deep Supervision Mechanism
IEEE Access, 2023 The segmentation of brain tumors is an important and challenging content in medical image processing. Relying solely on human experts to manually segment large volumes of data can be time-consuming and delay diagnosis.
Kittipol Wisaeng
doaj +1 more source
, 2022 Objectives: We developed a 3D U-Net-based deep convolutional neural network for the automatic segmentation of the vertebral cortex. The purpose of this study was to evaluate the accuracy of the 3D U-Net deep learning model.Methods: In this study, a fully
Shanshan Li (114847) +8 more
core +1 more source
Comparison of the U-Net, ResU-Net, and U-CARFnet performance.
, 2023 Comparison of the U-Net, ResU-Net, and U-CARFnet performance.
Chunhui Fan (16986055) +6 more
core +1 more source