相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article
Computer Science, Artificial Intelligence
Patrick Bilic et al.
Summary: This study reports the setup and results of the Liver Tumor Segmentation Benchmark (LiTS), which involved diverse image datasets and evaluated multiple segmentation algorithms. The best algorithms achieved high scores for liver segmentation, but varied performance for tumor segmentation. Further research is needed for tumor detection. LiTS remains an active benchmark and resource for liver-related segmentation tasks.
MEDICAL IMAGE ANALYSIS
(2023)
Article
Computer Science, Artificial Intelligence
Kai Han et al.
Summary: In this paper, efficient neural network designs for mobile devices are proposed. For CPU devices, a C-Ghost module is introduced to generate more feature maps, while for GPU devices, a G-Ghost stage structure is formulated to utilize stage-wise feature redundancy. Experimental results demonstrate the effectiveness of the proposed methods.
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Wenqiang Zhang et al.
Summary: This paper presents a mobile-friendly architecture called Token Pyramid Vision Transformer (TopFormer), which utilizes tokens from various scales to generate scale-aware semantic features and achieves a good trade-off between accuracy and latency.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2022)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Jeya Maria Jose Valanarasu et al.
Summary: UNeXt is a Convolutional multilayer perceptron (MLP) based network for medical image segmentation. It reduces the number of parameters, computational complexity, and improves segmentation performance through tokenized MLP blocks and channel shifting.
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT V
(2022)
Article
Computer Science, Interdisciplinary Applications
Jianpeng Zhang et al.
Summary: In this paper, we propose the 3D context residual network (ConResNet) for accurate segmentation of 3D medical images, which incorporates context residual modules and context attention mapping to improve segmentation accuracy. Experimental results show that the proposed model outperforms other methods in brain tumor and pancreas segmentation tasks. The network architecture is well-designed and demonstrates high reliability.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Computer Science, Artificial Intelligence
Changqian Yu et al.
Summary: Separating low-level details and high-level semantics is key to achieving high accuracy and efficiency in real-time semantic segmentation. The proposed architecture, called Bilateral Segmentation Network (BiSeNet V2), effectively handles feature representations through detail and semantics branches, striking a balance between speed and accuracy to outperform existing methods.
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Zihao Li et al.
Summary: This paper presents a novel Composite Context Fusion Network (CCF-Net) to jointly model intra-slice and inter-slice features, achieving state-of-the-art detection performance on multi-disease CT lesion detection task by excavating and exchanging information between texture-aware and context-aware features through stage-by-stage feature fusion.
2021 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Lei Li et al.
Summary: This study aims to improve 2D segmentation accuracy by leveraging consistency and discrepancy context information from adjacent slices, proposing a two-stage 2.5D segmentation framework based on U-Net. Experimental results demonstrate the effectiveness of the proposed methods in improving segmentation accuracy.
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT I
(2021)
Article
Computer Science, Artificial Intelligence
Matteo Dunnhofer et al.
MEDICAL IMAGE ANALYSIS
(2020)
Article
Computer Science, Interdisciplinary Applications
Zongwei Zhou et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2020)
Article
Computer Science, Interdisciplinary Applications
Zaiwang Gu et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2019)
Article
Computer Science, Artificial Intelligence
Xiahai Zhuang et al.
MEDICAL IMAGE ANALYSIS
(2019)