Related references
Note: Only part of the references are listed.nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Fabian Isensee et al.
NATURE METHODS (2021)
Efficient 3D Deep Learning Model for Medical Image Semantic Segmentation
Nasser Alalwan et al.
ALEXANDRIA ENGINEERING JOURNAL (2021)
A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises
S. Kevin Zhou et al.
PROCEEDINGS OF THE IEEE (2021)
A Multiple Layer U-Net, Un-Net, for Liver and Liver Tumor Segmentation in CT
Song-Toan Tran et al.
IEEE ACCESS (2021)
3D Liver and Tumor Segmentation with CNNs Based on Region and Distance Metrics
Yi Zhang et al.
APPLIED SCIENCES-BASEL (2020)
C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation
Qihang Yu et al.
2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2020)
Cascade U-ResNets for Simultaneous Liver and Lesion Segmentation
Xue-Feng Xi et al.
IEEE ACCESS (2020)
MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation
Tongle Fan et al.
IEEE ACCESS (2020)
Pulmonary nodule detection in CT scans with equivariant CNNs
Marysia Winkels et al.
MEDICAL IMAGE ANALYSIS (2019)
Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks
Tianjiao Liu et al.
MEDICAL IMAGE ANALYSIS (2019)
Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment
Patrick Schelb et al.
RADIOLOGY (2019)
SqueezeNAS: Fast Neural Architecture Search for Faster Semantic Segmentation
Albert Shaw et al.
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW) (2019)
Volumetric Attention for 3D Medical Image Segmentation and Detection
Xudong Wang et al.
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2019, PT VI (2019)
One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation
Mathias Perslev et al.
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2019, PT II (2019)
NAS-Unet: Neural Architecture Search for Medical Image Segmentation
Yu Weng et al.
IEEE ACCESS (2019)
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
Xiaomeng Li et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2018)
A Decomposable Model for the Detection of Prostate Cancer in Multi-parametric MRI
Nathan Lay et al.
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT II (2018)
3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes
Siqi Liu et al.
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT II (2018)
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Vijay Badrinarayanan et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2017)
Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation
Shuo Wang et al.
MEDICAL IMAGE ANALYSIS (2017)
Automated prostate cancer detection using T2-weighted and high-b-value diffusion-weighted magnetic resonance imaging
Jin Tae Kwak et al.
MEDICAL PHYSICS (2015)
Computer-Aided Detection of Prostate Cancer inMRI
Geert Litjens et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2014)
Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis
P. C. Vos et al.
PHYSICS IN MEDICINE AND BIOLOGY (2012)
A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification
K. Murphy et al.
MEDICAL IMAGE ANALYSIS (2009)
Pulmonary nodule detection in CT images with quantized convergence index filter
Surmaki Matsumoto et al.
MEDICAL IMAGE ANALYSIS (2006)
Share Paper
